CN110987183A - Multispectral imaging system and method - Google Patents

Multispectral imaging system and method Download PDF

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Publication number
CN110987183A
CN110987183A CN201911381603.XA CN201911381603A CN110987183A CN 110987183 A CN110987183 A CN 110987183A CN 201911381603 A CN201911381603 A CN 201911381603A CN 110987183 A CN110987183 A CN 110987183A
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image
vegetation index
reflectivity
multispectral imaging
band
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朱嘉炜
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Guangzhou Xaircraft Technology Co Ltd
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Guangzhou Xaircraft Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/2823—Imaging spectrometer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/2823—Imaging spectrometer
    • G01J2003/2826—Multispectral imaging, e.g. filter imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J2003/2866—Markers; Calibrating of scan
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1793—Remote sensing
    • G01N2021/1797—Remote sensing in landscape, e.g. crops
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/02—Mechanical
    • G01N2201/021—Special mounting in general
    • G01N2201/0214—Airborne

Abstract

The application relates to the technical field of unmanned aerial vehicles, and provides a multispectral imaging system and a multispectral imaging method, wherein the multispectral imaging system comprises a controller, and a multi-camera, an illumination sensor and a digital processor which are electrically connected with the controller; the controller is used for controlling the multi-camera to shoot a target plot when the unmanned aerial vehicle flies to a set shooting point to obtain a plurality of spectral images; the illumination sensor is used for measuring the illumination intensity value of each preset wave band; the digital processor is used for performing radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images, acquiring a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images according to the vegetation index, and performing index calculation to obtain a vegetation index map. The vegetation index map reflecting the plant information in the target plot can be automatically generated without manual participation, so that the manpower resource is saved, and the detection efficiency is improved.

Description

Multispectral imaging system and method
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to a multispectral imaging system and a multispectral imaging method.
Background
Along with the rapid development of the unmanned aerial vehicle industry, the application of the unmanned aerial vehicle in the aspect of agriculture is also wider and wider. The unmanned aerial vehicle remote sensing system is widely applied to the professional fields of farmland medicine spraying, farmland information monitoring, agricultural insurance investigation and the like by virtue of the advantages of convenience in carrying, high flexibility, short operation period and the like.
In the existing spectrum remote sensing technology, an unmanned aerial vehicle is usually adopted to carry a multispectral sensor to detect the spectral information of crops to obtain multispectral data, and then professionals perform subsequent analysis and processing on the data to obtain plant information, so that a large amount of manpower is consumed in the whole process.
Disclosure of Invention
The application aims to provide a multispectral imaging system and a multispectral imaging method, which are used for solving the problem that a large amount of manpower is consumed for detecting plant information by the existing spectrum remote sensing technology.
In order to achieve the above purpose, the embodiments of the present application employ the following technical solutions:
in a first aspect, the present application provides a multispectral imaging system, including a controller, and a multi-view camera, an illumination sensor, and a digital processor electrically connected to the controller; the controller is used for controlling the multi-camera to shoot a target plot when the unmanned aerial vehicle flies to a set shooting point to obtain a plurality of spectrum images, wherein each spectrum image corresponds to a different preset waveband; the illumination sensor is used for measuring the illumination intensity value of each preset waveband; the digital processor is used for carrying out radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images; the digital processor is further used for obtaining a target reflectivity image corresponding to the vegetation index from the multiple reflectivity images according to the vegetation index and carrying out index calculation to obtain a vegetation index map.
Optionally, the multispectral imaging system further comprises a color camera, and the color camera is electrically connected with the controller; the controller is also used for controlling the color camera to shoot the target plot at the same time of controlling the multi-camera to shoot so as to obtain a color image; the digital processor is further configured to image enhance each of the reflectance images with the color image.
Optionally, the plurality of preset bands comprise a green band, a red band and a near-infrared band; the multi-view camera is further used for shooting the target land parcel to obtain a spectrum image of a green light wave band, a spectrum image of a red side light wave band and a spectrum image of a near infrared wave band.
Optionally, the plurality of reflectance images include a reflectance image of a green light band, a reflectance image of a red light band, and a reflectance image of a near infrared band; the vegetation index comprises a normalized vegetation index corresponding to a red light band and a near infrared band, the vegetation index map comprises a normalized vegetation index map; the digital processor is further configured to obtain a reflectivity image of a red light waveband and a reflectivity image of a near-infrared waveband from the plurality of reflectivity images according to the normalized vegetation index, and perform index calculation on the reflectivity image of the red light waveband and the reflectivity image of the near-infrared waveband to obtain the normalized vegetation index map.
Optionally, the vegetation index comprises a normalized red-edge difference index corresponding to a red-edge band and a red-edge band, the vegetation index map comprises a normalized red-edge difference index map; the digital processor is further configured to obtain a reflectivity image of a red light band and a reflectivity image of a red light band from the plurality of reflectivity images according to the normalized red-edge difference index, and perform index calculation on the reflectivity image of the red light band and the reflectivity image of the red light band to obtain the normalized red-edge difference index map.
Optionally, the vegetation index comprises a soil vegetation index, the soil vegetation index corresponds to a red light band and an infrared light band, and the vegetation index map comprises a soil vegetation index map; the digital processor is further configured to obtain a reflectivity image of a red light waveband and a reflectivity image of an infrared light waveband from the plurality of reflectivity images according to the soil vegetation index, and perform index calculation on the reflectivity image of the red light waveband and the reflectivity image of the infrared light waveband to obtain the soil vegetation index map.
Optionally, the digital processor is further configured to acquire a relative positional relationship of each lens in the multi-view camera, and perform image alignment on each spectral image according to the relative positional relationship of each lens.
Optionally, the digital processor is further configured to perform background noise correction on each of the spectral images to remove background noise of each of the spectral images.
Optionally, the digital processor is further configured to obtain a lens distortion model parameter of the multi-view camera, and perform distortion correction on each spectral image according to the lens distortion model parameter, so as to remove distortion of each spectral image.
Optionally, the digital processor is further configured to perform vignetting effect correction on each of the spectral images to remove the vignetting effect of each of the spectral images.
Optionally, the multispectral imaging system further comprises a communication unit, wherein the communication unit is electrically connected with the controller and is in communication connection with a terminal; the communication unit is used for sending the vegetation index map to the terminal under the control of the controller.
Optionally, the multispectral imaging system further comprises a storage unit, and the storage unit is electrically connected with the controller; the storage unit is used for storing the vegetation index map under the control of the controller.
Optionally, the multispectral imaging system further comprises a display unit, and the display unit is electrically connected with the controller; the display unit is used for displaying the vegetation index map under the control of the controller.
In a second aspect, the present application further provides a multispectral imaging method, which is applied to a multispectral imaging system, where the multispectral imaging system includes a controller, and a multi-camera, an illumination sensor and a digital processor electrically connected to the controller; the multispectral imaging method comprises the following steps: when the unmanned aerial vehicle flies to a set photographing point, the controller controls the multi-camera to photograph a target plot to obtain a plurality of spectrum images, wherein each spectrum image corresponds to a different preset waveband; the illumination sensor measures the illumination intensity value of each preset waveband; the digital processor performs radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images; and the digital processor acquires a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map.
Optionally, the multispectral imaging system further comprises a color camera, and the color camera is electrically connected with the controller; the multispectral imaging method further comprises: the controller controls the color camera to shoot the target plot at the same time of controlling the multi-camera to shoot, so as to obtain a color image; the digital processor performs image enhancement on each of the reflectance images using the color images.
Optionally, the plurality of preset bands comprise a green band, a red band and a near-infrared band; the controller controls the multi-camera to shoot a target plot to obtain a plurality of spectral images, and the method comprises the following steps: the controller controls the multi-camera to shoot the target plot to obtain a spectrum image of a green light wave band, a spectrum image of a red side light wave band and a spectrum image of a near infrared wave band.
Optionally, the plurality of reflectance images include a reflectance image of a green light band, a reflectance image of a red light band, and a reflectance image of a near infrared band;
the vegetation index comprises a normalized vegetation index corresponding to a red light band and a near infrared band, the vegetation index map comprises a normalized vegetation index map; the digital processor obtains a target reflectivity image corresponding to the vegetation index from a plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps: and the digital processor acquires a reflectivity image of a red light wave band and a reflectivity image of a near infrared wave band from the plurality of reflectivity images according to the normalized vegetation index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the near infrared wave band to obtain the normalized vegetation index map.
Optionally, the vegetation index comprises a normalized red-edge difference index corresponding to a red-edge band and a red-edge band, the vegetation index map comprises a normalized red-edge difference index map; the digital processor obtains a target reflectivity image corresponding to the vegetation index from a plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps: and the digital processor acquires a reflectivity image of a red light wave band and a reflectivity image of a red light wave band from the plurality of reflectivity images according to the normalized red edge difference index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the red light wave band to obtain the normalized red edge difference index map.
Optionally, the vegetation index comprises a soil vegetation index, the soil vegetation index corresponds to a red light band and an infrared light band, and the vegetation index map comprises a soil vegetation index map; the digital processor obtains a target reflectivity image corresponding to the vegetation index from a plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps: and the digital processor acquires the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band from the plurality of reflectivity images according to the soil vegetation index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band to obtain the soil vegetation index map.
Optionally, before the step of measuring, by the illumination sensor, the illumination intensity value of each of the preset wavelength bands, the multispectral imaging method further includes: and the digital processor acquires the relative position relationship of each lens in the multi-view camera and aligns each spectral image according to the relative position relationship of each lens.
Optionally, before the step of measuring, by the illumination sensor, the illumination intensity value of each of the preset wavelength bands, the multispectral imaging method further includes: the digital processor performs background noise correction on each of the spectral images to remove background noise of each of the spectral images.
Optionally, before the step of measuring, by the illumination sensor, the illumination intensity value of each of the preset wavelength bands, the multispectral imaging method further includes: and the digital processor acquires lens distortion model parameters of the multi-view camera and performs distortion correction on each spectral image according to the lens distortion model parameters so as to remove distortion of each spectral image.
Optionally, before the step of measuring, by the illumination sensor, the illumination intensity value of each of the preset wavelength bands, the multispectral imaging method further includes: the digital processor performs vignetting effect correction on each of the spectral images to remove the vignetting effect of each of the spectral images.
Optionally, the multispectral imaging system further comprises a communication unit, wherein the communication unit is electrically connected with the controller and is in communication connection with a terminal; the multispectral imaging method further comprises: the communication unit sends the vegetation index map to the terminal under the control of the controller.
Optionally, the multispectral imaging system further comprises a storage unit, and the storage unit is electrically connected with the controller; the multispectral imaging method further comprises: the storage unit stores the vegetation index map under the control of the controller.
Optionally, the multispectral imaging system further comprises a display unit, and the display unit is electrically connected with the controller; the multispectral imaging method further comprises: the display unit displays the vegetation index map under the control of the controller.
Compared with the prior art, the multispectral imaging system and the multispectral imaging method are characterized in that the multispectral imaging system comprises a controller, a multi-camera, an illumination sensor and a digital processor, wherein the multi-camera, the illumination sensor and the digital processor are electrically connected with the controller; then, the digital processor performs radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images; and acquiring a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images according to the vegetation index and performing index calculation to obtain a vegetation index map. The vegetation index map reflecting the plant information in the target plot can be automatically generated without manual participation, so that the manpower resource is saved, and the detection efficiency is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a block diagram of a multispectral imaging system provided by an embodiment of the present application.
Fig. 2 shows an exemplary diagram of a photo spot provided in an embodiment of the present application.
Fig. 3 shows another block schematic diagram of a multispectral imaging system provided by an embodiment of the present application.
Fig. 4 shows another block schematic diagram of a multispectral imaging system provided by an embodiment of the present application.
Fig. 5 is a schematic flow chart of a multispectral imaging method according to an embodiment of the present disclosure.
Fig. 6 is a schematic flow chart of another multispectral imaging method provided by the embodiment of the present application.
Fig. 7 is a schematic flow chart of another multispectral imaging method provided by the embodiment of the present application.
Fig. 8 is a schematic flow chart of another multispectral imaging method provided by the embodiment of the present application.
Icon: 100-a multispectral imaging system; 110-a controller; 120-multi-view camera; 130-an illumination sensor; 140-a digital processor; 150-color camera; 160-a communication unit; 170-a storage unit; 180-display unit.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Along with the rapid development of the unmanned aerial vehicle industry, the application of the unmanned aerial vehicle in the aspect of agriculture is also wider and wider. The unmanned aerial vehicle remote sensing system is widely applied to the professional fields of farmland medicine spraying, farmland information monitoring, agricultural insurance investigation and the like by virtue of the advantages of convenience in carrying, high flexibility, short operation period and the like.
The spectrum remote sensing technology has the characteristics of high resolution, strong spectrum continuity, rich data information and the like, can dynamically, quickly, accurately and timely provide ground feature data information, and is an effective means for acquiring information in agriculture. The spectrum remote sensing technology can detect the physiochemical changes of the crop leaves in the growth process, the obtained spectrum data can be drawn into a spectrum response curve, and the spectrum response curve can reflect the contents and changes of chlorophyll, leaf water and trace elements in crops. Therefore, according to the characteristics of the spectrum remote sensing technology, the crop growth information can be known in time, and important references are provided for farmland medicine spraying, farmland information monitoring, agricultural insurance investigation and the like.
The existing spectrum remote sensing technology generally adopts an unmanned aerial vehicle to carry a multispectral sensor to detect the spectral information of crops to obtain multispectral data, and then professional personnel carry out subsequent analysis and processing on the data to obtain plant information, so that the whole process needs to consume a large amount of manpower, and the detection efficiency is low.
In order to solve the above problems, the present application provides a multispectral imaging system and method, which can automatically generate a vegetation index map reflecting plant information in a target plot without human intervention, thereby saving human resources and improving detection efficiency, and the following details are described.
Referring to fig. 1, fig. 1 is a block diagram illustrating a multispectral imaging system 100 according to an embodiment of the present disclosure. The multispectral imaging system 100 is applied to an unmanned aerial vehicle and comprises a controller 110, a multi-view camera 120, an illumination sensor 130 and a digital processor 140, wherein the controller 110 is electrically connected with the multi-view camera 120, the illumination sensor 130 and the digital processor 140.
The controller 110 is configured to control the multi-camera to shoot a target parcel when the unmanned aerial vehicle flies to a set shooting point, so as to obtain a plurality of spectral images, wherein each spectral image corresponds to a different preset waveband.
In this embodiment, the target plot refers to a plot in which the plant information needs to be detected by the unmanned aerial vehicle, and the target plot may be obtained by the plant protection operator by clicking or framing a corresponding area in the electronic map.
In the unmanned aerial vehicle detection process, the unmanned aerial vehicle can fly according to a pre-planned operation route, generally, the operation route covers the whole target plot, and in order to ensure that the unmanned aerial vehicle can detect plant information of each corner in the target plot, each photographing point is determined on the pre-planned operation route according to the flight speed and flight height of the unmanned aerial vehicle and by combining the route overlap rate, for example, please refer to fig. 2, the black origin in fig. 2 is the set photographing point, so that the unmanned aerial vehicle can position each photographing point in the flight process in Real time by an onboard positioning device (for example, a Real-time kinematic (RTK) positioning device) in the flight process, and when one photographing point is reached, the controller 110 is notified to trigger the multi-purpose camera 120 to photograph.
Generally, the vegetation is in the visible light band (400-. Meanwhile, due to the influence of chlorophyll absorption in the plant body, a steep hill climbing ridge is formed in the red to near infrared region (660-770 nm) of the reflection spectrum of the vegetation, and the ridge is called a red edge.
Therefore, the multi-view camera 120 can be set to capture the spectrum images of the green light band (520 + 600nm), the red light band (630 + 690nm), the red edge light band (660-770 nm) and the near infrared band (760 + 900nm), that is, the plurality of preset bands include the green light band, the red edge light band and the near infrared band.
Optionally, the controller 110 is also used to control and manage other hardware in the multispectral imaging system 100, such as the multi-view camera 120, the illumination sensor 130, and the digital processor 140. The controller 110 may be an embedded ARM, a single chip microcomputer, a Micro Controller Unit (MCU), a Complex Programmable Logic Device (CPLD), a Field Programmable Gate Array (FPGA), or other chips.
The multi-view camera 120 is used for shooting a target plot to obtain a plurality of spectral images.
In this embodiment, the plurality of spectrum images may include a spectrum image of a green light band, a spectrum image of a red side light band, and a spectrum image of a near infrared band, and therefore, the multi-view camera 120 is further configured to photograph the target land, obtain the spectrum image of the green light band, the spectrum image of the red side light band, and the spectrum image of the near infrared band, and send the plurality of spectrum images to the controller 110.
Optionally, the multi-view camera 120 is disposed on the side of the drone facing the plant, and captures different bands reflected by the plant through 4 independent lenses: green light band, red light band, near infrared band, and red side light band. That is, the multi-view camera 120 may include 4 lenses having a global exposure function, for example, an AR0144 lens, and the like, and each lens respectively carries a filter with a different preset waveband, that is, the 4 lenses respectively carry filters with a green light waveband (520-.
Optionally, the wavelength of the filter carried by each lens is within the corresponding wavelength band, for example, 4 lenses carry filters with wavelengths of 550nm, 660nm, 735nm, and 790nm, respectively, and are not limited herein.
The illumination sensor 130 is configured to measure an illumination intensity value of each of the preset wavelength bands.
In this embodiment, the illumination sensor 130 is disposed at a side of the drone facing the sky, and is configured to detect an illumination intensity value of each of the preset wavelength bands, i.e., a green light wavelength band, a red light wavelength band, and a near infrared wavelength band, and transmit the detected illumination intensity value of each of the preset wavelength bands to the controller 110.
Optionally, the illumination sensor 130 may carry 4 filters with different wavelength bands, that is, a filter with a green light wavelength band, a filter with a red side light wavelength band, and a filter with a near infrared wavelength band (760 and 900nm), and the 4 filters are used to measure the illumination intensity value of each corresponding wavelength band respectively. The wavelengths of the 4 filters carried by the illumination sensor 130 and the wavelengths of the filters carried by the 4 lenses of the multi-view camera 120 are in one-to-one correspondence, for example, 550nm, 660nm, 735nm, 790 nm.
Optionally, before the light sensor 130 leaves the factory, the light sensor 130 may perform radiation calibration, that is, a radiation conversion relationship is established by using a spectrometer, where the radiation conversion relationship includes a plurality of wavelengths and a plurality of conversion efficiencies, and the plurality of wavelengths and the plurality of conversion efficiencies correspond to each other one to one. In practical applications, when light with a specific wavelength reaches the illumination sensor 130, the illumination sensor 130 converts the illumination intensity value into a voltage value, i.e., U ═ I × a, where U denotes the voltage value, I denotes the illumination intensity value, and a denotes the conversion efficiency; then, determining the conversion efficiency corresponding to the specific wavelength by combining a pre-established radiation conversion relation; and then the illumination intensity value of the specific wavelength can be obtained according to the voltage value and the conversion efficiency.
The digital processor 140 is configured to perform radiation correction on each spectral image according to the illumination intensity value of each preset waveband, so as to obtain a plurality of reflectivity images.
In the present embodiment, after the multi-camera 120 sends the captured multiple spectral images to the controller 110, and the illumination sensor 130 sends the measured illumination intensity value of each preset wavelength band to the controller 110, the controller 110 sends both the multiple spectral images and the illumination intensity value of each preset wavelength band to the digital processor 140 for radiation correction. The plurality of reflectance images may include a reflectance image of a green wavelength band, a reflectance image of a red wavelength band, and a reflectance image of a near infrared wavelength band.
In this embodiment, due to the response characteristics of the multi-view camera 120 and the influence of absorption, scattering and other random factors of the atmosphere, the spectral image may be blurred and distorted, and the resolution and contrast of the image may be relatively reduced, so that it is necessary to perform restoration by radiation correction, that is, to eliminate various distortions adhering to the radiation brightness in the spectral image, and restore the original view of the spectral image as much as possible, so as to ensure the accuracy of plant information detection.
Optionally, the radiation correction may be performed on the spectral image corresponding to each preset waveband according to the illumination intensity value of each preset waveband, and taking the illumination intensity value of one preset waveband and the corresponding spectral image as an example, the process of performing radiation correction on the spectral image according to the illumination intensity value may include:
first, calculating a radiance value of the spectral image, that is, converting a pixel gray value of a corresponding band of the spectral image into a radiance value, which can be calculated by using equation (1):
L＝GAIN*DN+BIAS (1)
wherein, L represents the radiance value, GAIN represents the scaling slope, BIAS represents the scaling intercept, DN represents the pixel gray value, GAIN and BIAS can be calculated by the maximum radiance value Lmax and the minimum radiance value Lmin of the corresponding wave band in the spectral image, and GAIN ═ L (L ═ andmax-Lmin)/255，BIAS＝Lmin。
then, a radiation transmission model established by using the radiation transmission principle of electromagnetic waves in the atmosphere is used for performing atmospheric correction on the spectral image, and the spectral reflectivity of the ground object is calculated, wherein the radiation transmission model can be a 6S model as shown in formula (2):
Figure BDA0002342398950000121
wherein L represents a radiance value, ESRepresenting the value of solar radiation, thetasRepresenting the zenith angle of the sun, thetavRepresenting the solar azimuth angle, T () representing the atmospheric transmittance, phisRepresenting the zenith angle of the lens, phivRepresenting the angle of observation of the lens, pαRepresenting the reflectivity of pixel points of the spectral image, rho representing the path reflectivity, and S representing the spherical reflectivity;
and finally, correcting the gray value of each pixel point in the spectral image according to the calculated spectral reflectivity of the ground object, so as to obtain a corresponding reflection image.
Alternatively, the Digital processor 140 is an integrated circuit chip with Signal Processing capability, and the Digital processor 140 may be a dual-core Digital Signal Processing (DSP), FPGA, Graphics Processing Unit (GPU), or the like.
In this embodiment, the digital processor 140 may adopt a dual-core DSP chip, the dual-core DSP chip adopts a shared memory manner, the processing result of each core is stored in the shared memory, the processing task of each core is issued by the controller 110, and the index calculation is processed by the core with strong floating point calculation capability.
The digital processor 140 is further configured to obtain a target reflectivity image corresponding to the vegetation index from the multiple reflectivity images according to the vegetation index, and perform index calculation to obtain a vegetation index map.
In this embodiment, the vegetation index map is an index that is formed by combining spectral data of different bands and can reflect the growth condition of plants. For example, the plant leaf surface has strong absorption characteristics in a red light band, which cannot truly and objectively reflect the growth state of the plant, but has strong reflection characteristics in a near infrared band, so that the spectral data of the red light band and the spectral data of the near infrared band can be combined.
The vegetation index may include a normalized vegetation index (NDVI), a normalized red-edge difference index (NDRE), a soil vegetation index (SAVI), and the like, and correspondingly, the vegetation index map may include a normalized vegetation index map, a normalized red-edge difference index map, a soil vegetation index map, and the like. The vegetation index can be flexibly selected according to the vegetation information to be detected, for example, NDVI is adopted to detect the plant coverage, SAVI is adopted to detect the soil regulation, NDRE is adopted to detect the plant health degree, and the like; developers can select specific vegetation indexes according to actual needs, for example, the vegetation indexes are used for detecting plant diseases and insect pests, nutrition, fertilizers, water quality and the like, and the vegetation indexes are not limited herein.
In one embodiment, when the vegetation index is a normalized vegetation index, the normalized vegetation index corresponds to a red band and a near infrared band, and the vegetation index map includes a normalized vegetation index map. The digital processor is further used for obtaining the reflectivity image of the red light wave band and the reflectivity image of the near infrared wave band from the plurality of reflectivity images according to the normalized vegetation index, and performing index calculation on the reflectivity image of the red light wave band and the reflectivity image of the near infrared wave band to obtain a normalized vegetation index map.
In this embodiment, the NDVI is calculated from the visible light and the near-infrared light reflected by the vegetation, that is, the sum of the difference ratio between the reflection value of the near-infrared band and the reflection value of the red-light band, because healthy vegetation absorbs most of the visible light and reflects most of the near-infrared light, unhealthy or sparse vegetation reflects more visible light and less near-infrared light, and thus, the NDVI can be used for detecting the vegetation growth state and vegetation coverage, eliminating partial radiation errors, and the like.
The process of performing index calculation on the reflectance image of the red light band and the reflectance image of the near infrared band to obtain the normalized vegetation index map may include:
and calculating the normalized vegetation index of each pixel according to a formula NDVI (x, y) — (NIR (x, y) -RED (x, y))/(NIR (x, y) + RED (x, y)), so as to obtain a normalized vegetation index map, wherein (x, y) represents the coordinates of the pixel, NDVI (x, y) represents the normalized vegetation index, NIR (x, y) represents the reflectivity image of the near infrared band, and RED (x, y) represents the reflectivity image of the RED light band.
As another embodiment, when the vegetation index is a normalized red-edge difference index, the normalized red-edge difference index corresponds to a red light band and a red-edge light band, and the vegetation index map includes a normalized red-edge difference index map. The digital processor is further used for obtaining a reflectivity image of a red light wave band and a reflectivity image of a red light wave band from the multiple reflectivity images according to the normalized red edge difference index, and performing index calculation on the reflectivity image of the red light wave band and the reflectivity image of the red light wave band to obtain a normalized red edge difference index map.
In this embodiment, the NDRE may be used to analyze the health or non-health of vegetation in the image, which is similar to the normalized difference vegetation index, but is calculated using the reflectance image for the red band and the reflectance image for the red-side band.
The process of performing index calculation on the reflectance image of the red light band and the reflectance image of the red edge light band to obtain the normalized red edge difference index map may include: and calculating the normalized RED-edge difference index of each pixel according to a formula NDRE (x, y) — (REDDGE (x, y) -RED (x, y))/(REDDGE (x, y) + RED (x, y)), so as to obtain a normalized RED-edge difference index map, wherein NDRE (x, y) represents the normalized RED-edge difference index, and REDDGE (x, y) represents the reflectivity image of the RED-edge optical band.
As another embodiment, when the vegetation index includes a soil vegetation index, the soil vegetation index corresponds to a red light band and an infrared light band, and the vegetation index map includes a soil vegetation index map. The digital processor is further used for obtaining the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band from the multiple reflectivity images according to the soil vegetation index, and performing index calculation on the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band to obtain a soil vegetation index map.
In this embodiment, the process of performing index calculation on the reflectance image of the red light band and the reflectance image of the infrared light band to obtain the soil vegetation index map may include:
and calculating the normalized RED edge difference index of each pixel according to a formula SAVI (x, y) — (NIR (x, y) -RED (x, y))/(NIR (x, y) + RED (x, y) × (1+ L)), so as to obtain a normalized RED edge difference index map, wherein SAVI (x, y) represents the normalized RED edge difference index, and L represents the soil regulation coefficient.
Compared with NDVI, the SAVI is added with a soil regulation coefficient L determined according to actual conditions, and the value range is 0-1; when L is 0, the vegetation coverage is zero; when L is 1, the influence of the soil background is zero, that is, the vegetation coverage is very high; the effect of the soil background is zero, which only occurs where the tree is covered by tall trees with a dense crown.
Optionally, in order to improve the detection accuracy of the plant information, the color image may be used to enhance the spectral details of each reflectivity image, and therefore, referring to fig. 3 on the basis of fig. 1, the multispectral imaging system 100 further includes a color camera 150, and the color camera 150 is electrically connected to the controller 110.
The controller 110 is further configured to control the color camera 150 to shoot the target parcel to obtain the color image at the same time when the multi-view camera 120 is controlled to shoot.
In this embodiment, when the unmanned aerial vehicle flies to the set photographing point, the controller 110 may trigger and control the multi-view camera 120 and the color camera 150 to photograph simultaneously at the same time, that is, a plurality of spectral images and color images are photographed at the same time.
The color camera 150 is used for shooting a target land, so as to obtain a color image.
In this embodiment, the color camera 150 is disposed on a side of the unmanned aerial vehicle facing the plant, and is configured to capture the target plot, obtain a color image, and send the color image to the controller 110.
The digital processor 140 is also operative to image enhance each of the reflectance images with the color images.
In the present embodiment, after the color camera 150 transmits the captured color image to the controller 110, the controller 110 transmits the color image to the digital processor 140 so that the digital processor 140 performs image enhancement on each reflectance image using the color image. The digital processor 140 may fuse the color image with each reflectance image separately to enhance spectral detail in each reflectance image.
Alternatively, taking a color image and a reflectivity image as an example, the process of performing image enhancement on the reflectivity image by using the color image may include:
firstly, performing principal component transformation on each waveband of the reflectivity image, wherein a first principal component after transformation contains the same information of each waveband before transformation, and the part of each waveband, which uniquely corresponds to each waveband, is distributed to other wavebands after transformation;
then, histogram matching is carried out on the color image and the first principal component, so that the color image and the first principal component have similar mean value and variance;
and finally, the color image matched with the histogram is used for replacing the first principal component to carry out inverse transformation, and the reflectivity image with enhanced spectral details can be obtained.
Optionally, taking a color image and a reflectivity image as an example, the process of performing image enhancement on the reflectivity image by using the color image may further include: and combining the color image as a wave band and the reflectivity image for KL transformation, and distributing the transformed image information to realize the fusion of the color image and the reflectivity image.
Further, the spectral images captured by the multi-view camera 120 may appear blurry, distorted, etc. due to the influence of external factors and the lens itself, and therefore, in order to improve the detection accuracy, before the digital processor 140 performs radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectance images, the digital processor 140 further needs to perform preprocessing on each spectral image, including image alignment, background noise correction, distortion correction, vignetting effect correction, etc., and a specific correction sequence may be flexibly set by a developer, which is not limited herein and is described in detail below.
The digital processor 140 is further configured to obtain a relative position relationship of each lens in the multi-view camera, and perform image alignment on each spectral image according to the relative position relationship of each lens.
The digital processor 140 is further configured to perform background noise correction on each of the spectral images to remove the background noise of each of the spectral images.
In this embodiment, the spectral images may be subjected to background noise correction by means of mean filtering, median filtering, gaussian filtering, bilateral filtering, adaptive wiener filtering, and the like, so as to remove the background noise of each spectral image. Optionally, taking median filtering as an example, the original pixel point of each pixel point in the spectral image may be replaced by a median, that is, for the current pixel point, a template is first selected, and the template is composed of all pixel points in the neighborhood window of the current pixel point; then, the median values of all pixel points in the template are obtained, and the original pixel values of the current pixel points are replaced by the median values; and traversing each pixel point in the spectral image to complete the median filtering of the spectral image.
The digital processor 140 is further configured to obtain lens distortion model parameters of the multi-view camera, and perform distortion correction on each spectral image according to the lens distortion model parameters to remove distortion of each spectral image.
In this embodiment, the multi-view camera 120 includes 4 lenses, each lens has a corresponding lens distortion model parameter, each lens distortion model parameter is related to an actual distortion model of the corresponding lens, and the actual distortion model may be a Brown-Conrady (Brown-Conrady) distortion model, a polynomial model, a division model, or the like. Meanwhile, each lens distortion model parameter may be the same or different. When 4 shots of the multi-view camera 120 are produced in the same batch or have the same process parameters, the actual distortion model of each shot can be considered to be the same, so that the parameters of the shot distortion model of each shot can be considered to be the same.
The digital processor 140 is further configured to perform vignetting effect correction on each spectral image to remove the vignetting effect of each spectral image.
In the present embodiment, when the multi-view camera 120 performs long-distance imaging, a vignetting effect is easily generated, which causes uneven gray distribution of the spectral image, that is, as the field angle increases, the cross-sectional area of the oblique light beam that can pass through the objective lens will gradually decrease, which causes brightness in the middle and brightness at the edge of the generated spectral image, which affects the imaging quality, and therefore, the vignetting effect correction needs to be performed on each spectral image to remove the vignetting effect of the spectral image.
Referring to fig. 4, based on fig. 1, the multispectral imaging system 100 further includes a communication unit 160, a storage unit 170, and a display unit 180, wherein the communication unit 160, the storage unit 170, and the display unit 180 are electrically connected to the controller 110.
The communication unit 160 is configured to transmit the vegetation index map to the terminal under the control of the controller 110.
In this embodiment, the communication unit 160 is in communication connection with a terminal through a network, and the terminal may be a server, a ground workstation, a smart phone, a tablet computer, a laptop computer, a desktop computer, or the like. After the digital processor 140 generates the vegetation index map, the vegetation index map may be transmitted to the controller 110, and the controller 110 then controls the communication unit 160 to transmit the vegetation index map to the terminal.
Alternatively, the communication unit may be a WIFI device, a 4G device, or the like, or any other chip capable of communicating with the terminal.
The storage unit 170 is used to store the plant index map under the control of the controller 110.
In this embodiment, after the digital processor 140 generates the vegetation index map, the vegetation index map may be sent to the controller 110, and the controller 110 sends the vegetation index map to the storage unit 170 for storage.
Alternatively, the storage unit 170 may be a Random Access Memory (RAM), a non-volatile Memory (NVM), and the like.
The display unit 180 is used to display the vegetation index map under the control of the controller 110.
In this embodiment, after the digital processor 140 generates the vegetation index map, the vegetation index map may be sent to the controller 110, and the controller 110 controls the display unit 180 to display the vegetation index map.
Compared with the prior art, the method has the following beneficial effects:
firstly, a vegetation index map reflecting plant information in a target plot can be automatically generated without manual participation, so that human resources are saved, and the detection efficiency is improved;
secondly, a corresponding vegetation index map can be calculated in real time according to vegetation indexes related to the plant growth state, such as normalized vegetation index (NDVI), normalized red edge difference index (NDRE), soil vegetation index (SAVI) and the like, so that the subsequent application is facilitated;
thirdly, the spectral image of the corresponding waveband is subjected to radiation correction according to the illumination intensity value measured by the illumination sensor 130, so that the reflectivity stability of the multi-view camera 120 can be improved, and therefore, the reflectivity fluctuation of the multi-view camera 120 can be ensured to be small in both sunny days and cloudy days;
fourthly, the color image shot by the color camera is adopted to enhance the spectral details of each reflectivity image, so that the detection accuracy of the plant information can be improved;
fifth, the embedded controller 110 and digital processor 140 are used, which is light in weight and can realize spectral imaging of multiple bands.
Referring to fig. 5, a multispectral imaging method applied to the multispectral imaging system 100 is shown, and the multispectral imaging method may include the following steps:
step S101, when the unmanned aerial vehicle flies to a set photographing point, the controller controls the multi-camera to photograph a target object to obtain a plurality of spectrum images, wherein each spectrum image corresponds to a different preset waveband.
Optionally, the plurality of preset bands comprise a green band, a red band, and a near-infrared band; the mode that the controller control many meshes camera shoots and obtains a plurality of spectral image to the target parcel includes: the controller controls the multi-camera to shoot a target plot to obtain a spectrum image of a green light wave band, a spectrum image of a red side light wave band and a spectrum image of a near infrared wave band.
Step S102, the illumination sensor measures the illumination intensity value of each preset wave band.
And step S103, the digital processor performs radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images.
And S104, acquiring a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images by the digital processor according to the vegetation index, and performing index calculation to obtain a vegetation index map.
Optionally, the plurality of reflectance images include a reflectance image of a green wavelength band, a reflectance image of a red wavelength band, and a reflectance image of a near infrared wavelength band.
The vegetation index comprises a normalized vegetation index, the normalized vegetation index corresponds to a red light wave band and a near infrared wave band, and the vegetation index map comprises a normalized vegetation index map; the digital processor obtains a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps: and the digital processor acquires the reflectivity image of the red light wave band and the reflectivity image of the near infrared wave band from the plurality of reflectivity images according to the normalized vegetation index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the near infrared wave band to obtain a normalized vegetation index map.
Optionally, the vegetation index comprises a normalized red-edge difference index, the normalized red-edge difference index corresponds to a red light band and a red-edge light band, and the vegetation index map comprises a normalized red-edge difference index map; the digital processor obtains a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps: and the digital processor acquires the reflectivity image of the red light wave band and the reflectivity image of the red light wave band from the plurality of reflectivity images according to the normalized red edge difference index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the red light wave band to obtain a normalized red edge difference index map.
Optionally, the vegetation index comprises a soil vegetation index, the soil vegetation index corresponds to a red light band and an infrared light band, and the vegetation index map comprises a soil vegetation index map; the digital processor obtains a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps: and the digital processor acquires the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band from the plurality of reflectivity images according to the soil vegetation index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band to obtain a soil vegetation index map.
Referring to fig. 6 based on fig. 5, before step S102, the multispectral imaging method may further include step S111; after step S103, the multispectral imaging method may further include step S112.
And step S111, controlling the color camera to shoot the target object by the controller at the same time when the controller controls the multi-camera to shoot, so as to obtain a color image.
In step S112, the digital processor performs image enhancement on each reflectance image using the color image.
Referring to fig. 7, on the basis of fig. 5, before step S102, the multispectral imaging method may further include steps S121 to S124.
And step S121, the digital processor acquires the relative position relationship of each lens in the multi-view camera and aligns each spectral image according to the relative position relationship of each lens.
In step S122, the digital processor performs background noise correction on each spectral image to remove the background noise of each spectral image.
And S123, the digital processor acquires lens distortion model parameters of the multi-view camera, and distortion correction is carried out on each spectral image according to the lens distortion model parameters so as to remove distortion of each spectral image.
In step S124, the digital processor performs vignetting effect correction on each spectral image to remove the vignetting effect of each spectral image.
It should be noted that steps S121 to S124 may also be executed before step S102 of fig. 6.
Referring to fig. 8, after step S104, the multispectral imaging method may further include steps S105 to S107 based on fig. 5.
And step S105, the communication unit sends the vegetation index map to the terminal under the control of the controller.
And step S106, the storage unit stores the plant index map under the control of the controller.
In step S107, the display unit displays the vegetation index map under the control of the controller.
It should be noted that steps S121 to S124 may also be executed after step S104 of fig. 6, or after step S104 of fig. 7.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific processes of the multispectral imaging method described above may refer to the corresponding hardware function descriptions in the foregoing system embodiments, and are not described herein again.
In summary, the multispectral imaging system and method provided by the present application includes a controller, and a multi-view camera, an illumination sensor and a digital processor electrically connected to the controller; the controller is used for controlling the multi-camera to shoot a target plot when the unmanned aerial vehicle flies to a set shooting point to obtain a plurality of spectral images, wherein each spectral image corresponds to a different preset waveband; the illumination sensor is used for measuring the illumination intensity value of each preset wave band; the digital processor is used for carrying out radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images; the digital processor is further used for obtaining a target reflectivity image corresponding to the vegetation index from the multiple reflectivity images according to the vegetation index and carrying out index calculation to obtain a vegetation index map. The vegetation index map reflecting the plant information in the target plot can be automatically generated without manual participation, so that the manpower resource is saved, and the detection efficiency is improved.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

Claims (26)

1. The multispectral imaging system is characterized by comprising a controller, and a multi-view camera, an illumination sensor and a digital processor which are electrically connected with the controller;
the controller is used for controlling the multi-camera to shoot a target plot when the unmanned aerial vehicle flies to a set shooting point to obtain a plurality of spectrum images, wherein each spectrum image corresponds to a different preset waveband;
the illumination sensor is used for measuring the illumination intensity value of each preset waveband;
the digital processor is used for carrying out radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images;
the digital processor is further used for obtaining a target reflectivity image corresponding to the vegetation index from the multiple reflectivity images according to the vegetation index and carrying out index calculation to obtain a vegetation index map.
2. The multispectral imaging system of claim 1, wherein the multispectral imaging system further comprises a color camera, the color camera being electrically connected to the controller;
the controller is also used for controlling the color camera to shoot the target plot at the same time of controlling the multi-camera to shoot so as to obtain a color image;
the digital processor is further configured to image enhance each of the reflectance images with the color image.
3. The multispectral imaging system of claim 1, wherein a plurality of the predetermined wavelength bands comprise a green wavelength band, a red side wavelength band, and a near infrared wavelength band;
the multi-view camera is further used for shooting the target land parcel to obtain a spectrum image of a green light wave band, a spectrum image of a red side light wave band and a spectrum image of a near infrared wave band.
4. The multispectral imaging system of claim 1, wherein the plurality of reflectance images comprises a reflectance image in the green wavelength band, a reflectance image in the red wavelength band, and a reflectance image in the near infrared wavelength band;
the vegetation index comprises a normalized vegetation index corresponding to a red light band and a near infrared band, the vegetation index map comprises a normalized vegetation index map;
the digital processor is further configured to obtain a reflectivity image of a red light waveband and a reflectivity image of a near-infrared waveband from the plurality of reflectivity images according to the normalized vegetation index, and perform index calculation on the reflectivity image of the red light waveband and the reflectivity image of the near-infrared waveband to obtain the normalized vegetation index map.
5. The multispectral imaging system of claim 4, wherein the vegetation index comprises a normalized red-edge difference index corresponding to a red-edge band and a red-edge band, the vegetation index map comprising a normalized red-edge difference index map;
the digital processor is further configured to obtain a reflectivity image of a red light band and a reflectivity image of a red light band from the plurality of reflectivity images according to the normalized red-edge difference index, and perform index calculation on the reflectivity image of the red light band and the reflectivity image of the red light band to obtain the normalized red-edge difference index map.
6. The multispectral imaging system of claim 4, wherein the vegetation index comprises a soil vegetation index corresponding to a red band of light and an infrared band of light, the vegetation index map comprising a soil vegetation index map;
the digital processor is further configured to obtain a reflectivity image of a red light waveband and a reflectivity image of an infrared light waveband from the plurality of reflectivity images according to the soil vegetation index, and perform index calculation on the reflectivity image of the red light waveband and the reflectivity image of the infrared light waveband to obtain the soil vegetation index map.
7. The multispectral imaging system of claim 1, wherein the digital processor is further configured to obtain a relative positional relationship of each lens in the multispectral camera and to image-align each of the spectral images based on the relative positional relationship of each lens.
8. The multi-spectral imaging system of claim 1 wherein said digital processor is further configured to perform background noise correction on each of said spectral images to remove background noise from each of said spectral images.
9. The multispectral imaging system of claim 1, wherein the digital processor is further configured to obtain lens distortion model parameters for the multispectral camera and perform distortion correction on each of the spectral images based on the lens distortion model parameters to remove distortion from each of the spectral images.
10. The multi-spectral imaging system of claim 1 wherein said digital processor is further configured to apply vignetting correction to each of said spectral images to remove the vignetting effect of each of said spectral images.
11. The multispectral imaging system of claim 1, further comprising a communication unit in electrical communication with the controller and in communication with a terminal;
the communication unit is used for sending the vegetation index map to the terminal under the control of the controller.
12. The multispectral imaging system of claim 1, wherein the multispectral imaging system further comprises a memory unit, the memory unit being electrically connected to the controller;
the storage unit is used for storing the vegetation index map under the control of the controller.
13. The multispectral imaging system of claim 1, wherein the multispectral imaging system further comprises a display unit, the display unit being electrically connected to the controller;
the display unit is used for displaying the vegetation index map under the control of the controller.
14. The multispectral imaging method is applied to a multispectral imaging system, wherein the multispectral imaging system comprises a controller, and a multi-camera, an illumination sensor and a digital processor which are electrically connected with the controller;
the multispectral imaging method comprises the following steps:
when the unmanned aerial vehicle flies to a set photographing point, the controller controls the multi-camera to photograph a target plot to obtain a plurality of spectrum images, wherein each spectrum image corresponds to a different preset waveband;
the illumination sensor measures the illumination intensity value of each preset waveband;
the digital processor performs radiation correction on each spectral image according to the illumination intensity value of each preset waveband to obtain a plurality of reflectivity images;
and the digital processor acquires a target reflectivity image corresponding to the vegetation index from the plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map.
15. The method of multispectral imaging as recited in claim 14 wherein said multispectral imaging system further comprises a color camera, said color camera being electrically connected to said controller;
the multispectral imaging method further comprises:
the controller controls the color camera to shoot the target plot at the same time of controlling the multi-camera to shoot, so as to obtain a color image;
the digital processor performs image enhancement on each of the reflectance images using the color images.
16. The method of multispectral imaging according to claim 14, wherein a plurality of said predetermined wavelength bands comprise a green wavelength band, a red-edge wavelength band, and a near-infrared wavelength band;
the controller controls the multi-camera to shoot a target plot to obtain a plurality of spectral images, and the method comprises the following steps:
the controller controls the multi-camera to shoot the target plot to obtain a spectrum image of a green light wave band, a spectrum image of a red side light wave band and a spectrum image of a near infrared wave band.
17. The method of multispectral imaging of claim 14, wherein the plurality of reflectance images comprises a reflectance image in the green wavelength band, a reflectance image in the red wavelength band, and a reflectance image in the near infrared wavelength band;
the vegetation index comprises a normalized vegetation index corresponding to a red light band and a near infrared band, the vegetation index map comprises a normalized vegetation index map;
the digital processor obtains a target reflectivity image corresponding to the vegetation index from a plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps:
and the digital processor acquires a reflectivity image of a red light wave band and a reflectivity image of a near infrared wave band from the plurality of reflectivity images according to the normalized vegetation index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the near infrared wave band to obtain the normalized vegetation index map.
18. The multispectral imaging method of claim 17, wherein the vegetation index comprises a normalized red-edge difference index corresponding to a red-edge band and a red-edge band, the vegetation index map comprising a normalized red-edge difference index map;
the digital processor obtains a target reflectivity image corresponding to the vegetation index from a plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps:
and the digital processor acquires a reflectivity image of a red light wave band and a reflectivity image of a red light wave band from the plurality of reflectivity images according to the normalized red edge difference index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the red light wave band to obtain the normalized red edge difference index map.
19. The multi-spectral imaging method of claim 17 wherein the vegetation index comprises a soil vegetation index corresponding to a red band of light and an infrared band of light, the vegetation index map comprising a soil vegetation index map;
the digital processor obtains a target reflectivity image corresponding to the vegetation index from a plurality of reflectivity images according to the vegetation index and performs index calculation to obtain a vegetation index map, and the method comprises the following steps:
and the digital processor acquires the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band from the plurality of reflectivity images according to the soil vegetation index, and performs index calculation on the reflectivity image of the red light wave band and the reflectivity image of the infrared light wave band to obtain the soil vegetation index map.
20. The method of multispectral imaging as recited in claim 14 wherein, prior to the step of said light sensor measuring the light intensity values for each of said predetermined wavelength bands, said method of multispectral imaging further comprises:
and the digital processor acquires the relative position relationship of each lens in the multi-view camera and aligns each spectral image according to the relative position relationship of each lens.
21. The method of multispectral imaging as recited in claim 14 wherein, prior to the step of said light sensor measuring the light intensity values for each of said predetermined wavelength bands, said method of multispectral imaging further comprises:
the digital processor performs background noise correction on each of the spectral images to remove background noise of each of the spectral images.
22. The method of multispectral imaging as recited in claim 14 wherein, prior to the step of said light sensor measuring the light intensity values for each of said predetermined wavelength bands, said method of multispectral imaging further comprises:
and the digital processor acquires lens distortion model parameters of the multi-view camera and performs distortion correction on each spectral image according to the lens distortion model parameters so as to remove distortion of each spectral image.
23. The method of multispectral imaging as recited in claim 14 wherein, prior to the step of said light sensor measuring the light intensity values for each of said predetermined wavelength bands, said method of multispectral imaging further comprises:
the digital processor performs vignetting effect correction on each of the spectral images to remove the vignetting effect of each of the spectral images.
24. The multispectral imaging method of claim 14, wherein the multispectral imaging system further comprises a communication unit electrically connected to the controller and communicatively connected to a terminal;
the multispectral imaging method further comprises:
the communication unit sends the vegetation index map to the terminal under the control of the controller.
25. The multispectral imaging method of claim 14, wherein the multispectral imaging system further comprises a memory unit, the memory unit being electrically connected to the controller; the multispectral imaging method further comprises:
the storage unit stores the vegetation index map under the control of the controller.
26. The method of multispectral imaging as recited in claim 14 wherein said multispectral imaging system further comprises a display unit, said display unit being electrically connected to said controller; the multispectral imaging method further comprises:
the display unit displays the vegetation index map under the control of the controller.
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CN113808053A (en) * 2021-09-29 2021-12-17 华北电力大学（保定） Infrared imager and signal correction method thereof
CN113874716A (en) * 2020-04-15 2021-12-31 深圳市大疆创新科技有限公司 Operation method of movable platform, movable platform and electronic equipment
CN114500768A (en) * 2022-02-18 2022-05-13 广州极飞科技股份有限公司 Camera synchronous shooting method and device, unmanned equipment and storage medium

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110134138A1 (en) * 2008-06-06 2011-06-09 Monsanto Technology Llc Generating Agricultural Information Products Using Remote Sensing
CN203350719U (en) * 2013-07-03 2013-12-18 广州地理研究所 Multispectral remote sensing system of single rotor micro unmanned plane
CN204154389U (en) * 2014-09-30 2015-02-11 青岛市光电工程技术研究院 The micro-unmanned airborne multi-optical spectrum imaging system in a kind of low latitude
CN104730005A (en) * 2015-03-27 2015-06-24 中国农业科学院农业信息研究所 Ground-air integrated agricultural monitoring system and method
CN205861560U (en) * 2016-05-27 2017-01-04 中国农业科学院农业信息研究所 Farmland monitoring system
US20180065748A1 (en) * 2016-07-27 2018-03-08 International Business Machines Corporation Drone-based mosquito amelioration based on risk analysis and pattern classifiers
CN107807125A (en) * 2017-10-12 2018-03-16 中国农业大学 Plant information computing system and method based on UAV system multispectral sensor
CN108322670A (en) * 2018-04-27 2018-07-24 Oppo广东移动通信有限公司 A kind of control method of multi-camera system, mobile terminal and storage medium
CN110096995A (en) * 2019-04-28 2019-08-06 中控智慧科技股份有限公司 The multispectral more mesh camera Antiforge recognizing methods of one kind and device
CN110222475A (en) * 2019-07-03 2019-09-10 中国水利水电科学研究院 A method of based on unmanned plane multispectral remote sensing inverting winter wheat plant moisture content
CN110567891A (en) * 2019-09-16 2019-12-13 中国水利水电科学研究院 Winter wheat canopy chlorophyll estimation system and method
CN110580684A (en) * 2018-06-10 2019-12-17 长沙市军英电子科技有限公司 image enhancement method based on black-white-color binocular camera

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110134138A1 (en) * 2008-06-06 2011-06-09 Monsanto Technology Llc Generating Agricultural Information Products Using Remote Sensing
CN203350719U (en) * 2013-07-03 2013-12-18 广州地理研究所 Multispectral remote sensing system of single rotor micro unmanned plane
CN204154389U (en) * 2014-09-30 2015-02-11 青岛市光电工程技术研究院 The micro-unmanned airborne multi-optical spectrum imaging system in a kind of low latitude
CN104730005A (en) * 2015-03-27 2015-06-24 中国农业科学院农业信息研究所 Ground-air integrated agricultural monitoring system and method
CN205861560U (en) * 2016-05-27 2017-01-04 中国农业科学院农业信息研究所 Farmland monitoring system
US20180065748A1 (en) * 2016-07-27 2018-03-08 International Business Machines Corporation Drone-based mosquito amelioration based on risk analysis and pattern classifiers
CN107807125A (en) * 2017-10-12 2018-03-16 中国农业大学 Plant information computing system and method based on UAV system multispectral sensor
CN108322670A (en) * 2018-04-27 2018-07-24 Oppo广东移动通信有限公司 A kind of control method of multi-camera system, mobile terminal and storage medium
CN110580684A (en) * 2018-06-10 2019-12-17 长沙市军英电子科技有限公司 image enhancement method based on black-white-color binocular camera
CN110096995A (en) * 2019-04-28 2019-08-06 中控智慧科技股份有限公司 The multispectral more mesh camera Antiforge recognizing methods of one kind and device
CN110222475A (en) * 2019-07-03 2019-09-10 中国水利水电科学研究院 A method of based on unmanned plane multispectral remote sensing inverting winter wheat plant moisture content
CN110567891A (en) * 2019-09-16 2019-12-13 中国水利水电科学研究院 Winter wheat canopy chlorophyll estimation system and method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
孙刚 等: "轻小型无人机多光谱遥感技术应用进展", 《农业机械学报》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113874716A (en) * 2020-04-15 2021-12-31 深圳市大疆创新科技有限公司 Operation method of movable platform, movable platform and electronic equipment
CN112333359A (en) * 2020-10-28 2021-02-05 维沃移动通信（杭州）有限公司 Image processing method and device and electronic equipment
CN113358581A (en) * 2021-06-04 2021-09-07 长光禹辰信息技术与装备(青岛)有限公司 Unmanned aerial vehicle multispectral remote sensing data acquisition and processing integrated system and method
CN113588560A (en) * 2021-08-23 2021-11-02 广州思林杰科技股份有限公司 Multispectral acquisition terminal, system and method for citrus greening disease monitoring
CN113808053A (en) * 2021-09-29 2021-12-17 华北电力大学（保定） Infrared imager and signal correction method thereof
CN114500768A (en) * 2022-02-18 2022-05-13 广州极飞科技股份有限公司 Camera synchronous shooting method and device, unmanned equipment and storage medium
CN114500768B (en) * 2022-02-18 2023-06-16 广州极飞科技股份有限公司 Camera synchronous shooting method and device, unmanned equipment and storage medium

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