KR102175192B1 - System and Method for Cross Sectional Monitoring usig Spatio-temporal Hyperspectral Images - Google Patents

Abstract

The present invention relates to a cross section monitoring apparatus using spatiotemporal hyperspectral images and a method thereof which use spatiotemporal hyperspectral images in cross section monitoring to secure spectral properties at the same position at different times to effectively measure temporal changes in the same cross section. The cross section monitoring apparatus using spatiotemporal hyperspectral images comprises: a hyperspectral image acquisition device which has a hyperspectral sensor and records a hyperspectral image to provide the hyperspectral image; a spatiotemporal hyperspectral image processing unit to receive a hyperspectral image from the hyperspectral image acquisition unit to learn the correlation between an observation value corresponding to a portion of a photographed time existing on a cross section on which a hyperspectral image is collected and a spectral property index at a corresponding position to estimate a change of the observation value of the cross section during the entire time; and an observation value change analysis result output unit to output an observation value change analysis result of the spatiotemporal hyperspectral image processing unit to know the spatial distribution of the observation value over time.

Description

Sectional monitoring device and method using spatio-temporal hyperspectral images {System and Method for Cross Sectional Monitoring usig Spatio-temporal Hyperspectral Images}

The present invention relates to a remote sensing technology, and more specifically, a space-time hyperspectral image that enables effective measurement of temporal changes in the same cross-section by securing spectral characteristics over various times at the same location by utilizing a space-time hyperspectral image for cross-sectional monitoring. It relates to an apparatus and method for monitoring a section using an image.

Recently, the social interest related to water pollution such as green algae phenomena due to climate change and high temperatures in summer, and chemical substances and oil spills due to various accidents is increasing.

Among the water pollution cases, the leakage of hazardous chemicals due to a chemical accident adversely affects the human body when contacted, contaminates the air, water, and soil, and causes various and complex damages such as discoloration or necrosis of surrounding crops.

In particular, chemicals that may spill into rivers are often colorless and water-soluble, making it difficult to confirm the spill with the naked eye.

In the event of a chemical accident, chemical substances are directly detected by using simple detection equipment such as pH paper, simple identification detection kit, detection tube type detection equipment, electronic detection equipment portable ion spectrometer, portable FT-IR, etc.

Such a contact sensor is dependent on on-site personnel, and the installed detection sensor is also limitedly installed in a place where there is a concern about the leakage of chemical substances, so it is difficult to actively detect an uninstalled area.

If the accident substance is not specified, the initial response may be delayed or it may be extended to a fatal environmental disaster due to an incorrect response. Therefore, appropriate measures and responses are required in case of occurrence, and professional response is required for damage recovery.

The introduction of a remote sensing technique capable of overcoming the limitations of such a high-risk contact detection technology and the use of the latest sensor technology are being introduced.

Meanwhile, the prior art remote sensing technology utilizes a line scan hyperspectral sensor mounted on a mobile platform such as a satellite, an aircraft, and a drone, and a hyperspectral image captured through a camera as a distribution of spatial spectral characteristics at the same time.

Recently, hyperspectral images that can be installed and operated by satellites, aircraft, and drones are mounted on satellites, aircraft and drones, and have been started from studies on land cover, geological conditions, crop growth and disease, and soil moisture content. It is widely used in a wide range of fields such as defense, medical care, food, agriculture, geology and minerals, and environmental fields, but it can also be used for the possibility of detecting chemical substances.

More specifically, even in river environments, hyperspectral images have begun to be used when attempting to analyze the water capacity of rivers through satellite images or drone images.

There is a case study (Kisevic et al ., 2016) that develops a methodology for monitoring river water quality (Chl-a, TSS, Tubidity), etc. using hyperspectral images for aerial and satellite remote sensing. There was also a case of developing a hyperspectral remote sensing algorithm for water quality (Chl-a, CDOM, TSS) based on (Fan, 2014).

In addition, researches such as the development of river depth measurement technology (You, 2018) using drone-based hyperspectral images are being actively conducted.

However, in order to monitor the temporal change of the same point, the hyperspectral image must be photographed in the same space at various times, but it is difficult to photograph the temporal change at the same point due to the current technical limitations.

Accordingly, there is a need for the development of a new technology that enables effective measurement of temporal changes in the same cross section by securing spectral characteristics over time at the same location.

Republic of Korea Patent Publication No. 10-2020-0011727 Korean Patent Registration No. 10-1463354 Korean Patent Registration No. 10-1619836

The present invention is to solve the problem of the remote sensing technology of the prior art, by utilizing a spatiotemporal hyperspectral image for cross-section monitoring to secure spectral characteristics over various times at the same location, thereby effectively measuring the temporal change in the same cross-section. It is an object of the present invention to provide a cross-sectional monitoring apparatus and method using a space-time hyperspectral image.

In the present invention, the correlation between the observed value corresponding to a portion of the photographed time that exists in the section where the hyperspectral image is collected and the spectral characteristic index of the corresponding location is identified through learning, and the change in the observed value of the section over the entire time is determined. An object of the present invention is to provide an apparatus and method for monitoring a section using a spatiotemporal hyperspectral image capable of increasing measurement accuracy by estimating.

In the present invention, by mounting and operating a hyperspectral sensor and a camera on a fixed platform, it is possible to secure spectral characteristics over various times at the same location, and analyze the correlation between the accumulated spectral characteristics and the physical characteristics of the object to be observed. It is an object of the present invention to provide an apparatus and method for monitoring a section using a spatiotemporal hyperspectral image capable of accurately measuring a temporal change in the same section.

In the present invention, when a technology for securing spectral characteristics over various time periods at the same location by utilizing a space-time hyperspectral image for cross-sectional monitoring is applied to a depth adjacent to a river structure, the change in depth occurring near the structure can be temporally identified. It is an object of the present invention to provide a cross-sectional monitoring apparatus and method using a space-time hyperspectral image.

The present invention utilizes a spatiotemporal hyperspectral image for cross-sectional monitoring and combines a technology for securing spectral characteristics over various time periods at the same location with a technology for detecting chemical substances adjacent to a river structure, so that it is possible to easily determine whether a chemical substance is leaked from the river. It is an object of the present invention to provide a cross-sectional monitoring apparatus and method using a space-time hyperspectral image.

Other objects of the present invention are not limited to the objects mentioned above, and other objects that are not mentioned will be clearly understood by those skilled in the art from the following description.

A cross-sectional monitoring device using a space-time hyperspectral image according to the present invention for achieving the above object includes a hyperspectral image acquisition device including a hyperspectral sensor and recording and providing a hyperspectral image; a second from the hyperspectral image acquisition device It receives a spectral image and learns the correlation between the observed value corresponding to the part of the photographed time that exists in the section where the hyperspectral image was collected and the spectral characteristic index of the corresponding location to estimate the change in the observed value of the section over the entire time. And a spatiotemporal hyperspectral image processing unit; an observation value change analysis result output unit for outputting the observation value change analysis result of the spatiotemporal hyperspectral image processing unit so as to know a spatial distribution of observed values over time.

Here, the space-time hyperspectral image processing unit, with respect to the total number of wavelengths constituting the hyperspectral image, two wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) selected in consideration of the number of all cases in which two wavelengths are selected. A spectral characteristic extraction unit that extracts spectral characteristics for, a band ratio calculation unit that calculates a band ratio or a band index according to the definition of spectral characteristics, and a correlation coefficient that calculates a correlation coefficient through regression analysis of the observed values and spectral characteristics. It characterized in that it comprises a calculation unit, and a correlation coefficient map construction unit for constructing a correlation coefficient map by storing the magnitude of the correlation coefficient as a color at the positions of the ordered pair of wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ).

In addition, the hyperspectral image acquisition device is composed of a mobile platform using a drone, and is operated hydraulically at the center of the drone and equipped with a damper to mitigate vibrations or shocks that may occur in the drone or gimbal for movement during flight. And, a gimbal that keeps the same posture at the bottom of it so that shooting can be performed is connected, and the position and posture of the drone and the posture of the gimbal are accumulated and recorded in the line scan area to record the hyperspectral image. It characterized in that it comprises a hyperspectral sensor for recording the, and a camera for monitoring the state of the drone and shooting during flight.

And the shooting area of the hyperspectral image captured by the hyperspectral image acquisition device composed of a mobile platform using a drone can be defined as the length of the line projected on the water surface by the viewing angle of the hyperspectral sensor fixed around the drone, It is characterized in that it is a set of hyperspectral scanning lines.

In addition, the hyperspectral image provided by the hyperspectral image acquisition device is composed of a plurality of bands including a visible light region, an ultraviolet ray, and an infrared region, and the latitude and longitude of the location information of the pixels constituting the hyperspectral image are recorded. It is characterized.

In addition, the space-time hyperspectral image processing unit is characterized in that by using a relational expression between spectral characteristics and observed values to configure a change in a physical quantity of a section over time into a spatial axis and a time axis.

In addition, the observation value change analysis result output unit is characterized in that it provides to know the spatial distribution of the observation value over time, so that the observation value change at the same point (Pixel Point) can be recognized.

A cross-sectional monitoring method using a spatiotemporal hyperspectral image according to the present invention for achieving another object includes the steps of acquiring a hyperspectral image from a hyperspectral image acquisition device; receiving a hyperspectral image from the hyperspectral image acquisition device to obtain a hyperspectral image A spatiotemporal hyperspectral image processing step of estimating a change in the observed value of the section over the entire time by learning the correlation between the observed value corresponding to a portion of the photographed time existing in the collected section and the spectral characteristic index of the corresponding location; And outputting, by the change analysis result output unit, the result of the analysis of the change of the observed value so as to know the spatial distribution of the observed value over time.

Here, in the space-time hyperspectral image processing step, two wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) selected in consideration of the number of all the wavelengths constituting the hyperspectral image, in all cases of selecting two wavelengths. Extracting the spectral characteristic for, calculating the band ratio or band index according to the definition of the spectral characteristic, calculating the correlation coefficient through regression analysis of the observed value and the spectral characteristic, and wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) and storing the magnitude of the correlation coefficient as a color at the position of the ordered pair, and constructing a correlation coefficient map.

And in order to calculate the spectral characteristics for the location where the observed value exists from the hyperspectral image,

로 밴드비를 정의하거나,

로 지수형태로 분광특성을 정의하고,

는 파장(

)에 대한 빛의 세기인 것을 특징으로 한다.

Define the band ratio as

To define the spectral characteristics in exponential form,

Is the wavelength(

) Is the intensity of light.

The cross-sectional monitoring apparatus and method using a space-time hyperspectral image according to the present invention as described above has the following effects.

First, the spatiotemporal hyperspectral image is used for cross-sectional monitoring to secure spectral characteristics over various times at the same location so that temporal changes in the same cross-section can be effectively measured.

Second, the correlation between the observed value corresponding to the part of the photographed time that exists in the section where the hyperspectral image is collected and the spectral characteristic index of the corresponding location is identified through learning, and the change in the observed value of the section over the entire time is estimated. This makes it possible to increase the measurement accuracy.

Third, by installing and operating a hyperspectral sensor and camera on a fixed platform, it is possible to secure spectral characteristics over various times at the same location, and through correlation analysis between the accumulated spectral characteristics and the physical characteristics of the object to be observed. It allows precise measurement of temporal changes in the same section.

Fourth, in the case of applying a technology that secures spectral characteristics over various time periods at the same location by using space-time hyperspectral images for cross-sectional monitoring, changes in water depth occurring near the structure can be identified in time. do.

Fifth, by utilizing the spatiotemporal hyperspectral image for cross-sectional monitoring, the technology of securing the spectral characteristics over various times at the same location is combined with the detection technology of chemical substances adjacent to the river structure, so that it is possible to easily identify whether chemical substances are leaking from the river. do.

1 is a configuration diagram of a preferred hyperspectral image acquisition system according to the present invention
Figure 2 is a block diagram of a cross-section monitoring device using a space-time hyperspectral image according to the present invention
3 is a flow chart showing a cross-sectional monitoring method using a space-time hyperspectral image according to the present invention
4 and 5 are configuration diagrams showing a hyperspectral image acquisition method according to the present invention
6 is a block diagram showing a hyperspectral image and location information acquired by a drone-based hyperspectral sensor
7 is a block diagram showing a method of calculating a spectral characteristic for a location where an observation value exists from a hyperspectral image
8 is a block diagram showing a process of constructing a correlation coefficient map
9 is a block diagram showing a change in physical quantity of a cross section over time using a relational expression between spectral characteristics and observed values
10 is a block diagram showing the spatial distribution of observed values over time
11 is a block diagram showing changes in observed values at an arbitrary point over time

Hereinafter, a preferred embodiment of a cross-sectional monitoring apparatus and method using a space-time hyperspectral image according to the present invention will be described in detail.

Features and advantages of the cross-sectional monitoring apparatus and method using a space-time hyperspectral image according to the present invention will become apparent through detailed descriptions of each embodiment below.

1 is a block diagram of a preferred hyperspectral image acquisition system according to the present invention.

The cross-section monitoring apparatus and method using a spatiotemporal hyperspectral image according to the present invention utilizes a spatiotemporal hyperspectral image for cross-sectional monitoring to secure spectral characteristics over various times at the same location so that the temporal change in the same cross-section can be effectively measured. I did it.

To this end, the present invention investigates the correlation between the observation value corresponding to a part of the photographed time present in the section where the hyperspectral image is collected and the spectral characteristic index of the corresponding location through learning, and the observation value of the section over the entire time. It may include a configuration that estimates the change of.

In the present invention, by mounting and operating a hyperspectral sensor and a camera on a fixed platform, it is possible to secure spectral characteristics over various times at the same location, and analyze the correlation between the accumulated spectral characteristics and the physical characteristics of the object to be observed. Through it, a configuration for accurately measuring a temporal change in the same cross section may be included.

A general RGB camera photographs a target area in one frame using an area scan method that scans a cross section.

On the other hand, hyperspectral cameras use a line scan method that photographs using a line sensor consisting of a line, and the line scan type hyperspectral sensor uses a whisk broom and push brom depending on the method of recording spectral characteristics. broom) method.

While the whisk broom method photographs an object while the reflector rotates, the push broom method records light entering through the prism by arranging optical sensors in a row.

The data collected in this way form a data cube. In the case of RGB images, a simple data cube is formed, whereas a hyperspectral image contains vast spectral information in various wavelength bands for each pixel (point), so a very large data cube is formed. Formed.

The present invention is to provide a technology capable of identifying and detecting dissolved or suspended chemical substances based on hyperspectral images taken by being mounted on a drone in a situation in which hazardous chemical substances are leaked from a river.

For this, the cross-sectional monitoring apparatus using a space-time hyperspectral image according to the present invention may use a platform as shown in FIG. 1.

1 shows a platform capable of performing a preferred embodiment of the present invention, and shows a drone-based hyperspectral image acquisition system.

As shown in FIG. 1, the hyperspectral image acquisition system according to the present invention mounts a damper 20 at the center of the drone and connects the gimbal 10 to the lower portion thereof, and the gimbal 10 It is a mobile platform having a structure in which the hyperspectral sensor 40 and a separate camera 30 are installed.

The shock absorber 20 is hydraulically operated and also prevents overload of the gimbal 10 that controls the posture by mitigating vibrations or shocks that may occur on the drone or gimbal for movement during flight, and at the same time, the quality of the hyperspectral sensor It contributes to improvement.

The gimbal 10 always maintains the same posture, so that the hyperspectral sensor 40 and the camera 30 can be photographed in the same posture even with vibration and shock.

The installed hyperspectral sensor 40 is responsible for recording hyperspectral images by accumulating and recording the line scan area through the position and posture of the drone and the posture of the gimbal, and the camera 30 is for monitoring the drone and shooting status during flight. It is installed for.

2 is a block diagram of a cross-section monitoring apparatus using a space-time hyperspectral image according to the present invention.

A cross-sectional monitoring device using a space-time hyperspectral image according to the present invention includes a hyperspectral sensor, as shown in FIG. 2, and records a hyperspectral image by accumulating and recording line scan areas through the position and posture of the drone and the posture of the gimbal. An observation value corresponding to a portion of the photographed time existing in the section in which the hyperspectral image acquisition device 100 and the hyperspectral image acquisition device 100 are provided and the hyperspectral image is collected and the corresponding position The spatiotemporal hyperspectral image processing unit 200 that estimates the change of the observed value of the cross section over the entire time by learning the correlation of the spectral characteristic index of, and the spatiotemporal hyperspectral image processing unit 200 outputting the analysis result of the observed value change. And an observation value change analysis result output unit 300.

In this way, when the hyperspectral sensor and camera are mounted and operated on a fixed platform, spectral characteristics can be secured at the same location over various time periods, and correlation analysis between the accumulated spectral characteristics and the physical characteristics of the object to be observed can be performed. As a result, it is possible to measure the temporal change in the same cross section.

When the cross-sectional monitoring device using a space-time hyperspectral image according to the present invention is applied to a depth adjacent to a river structure, it is possible to grasp the change in depth occurring in the vicinity of the structure in time, and the chemical substance detection technology adjacent to the river structure and In the case of grafting, it is possible to accurately determine whether a chemical substance is leaked from the river.

A detailed configuration of the space-time hyperspectral image processing unit 200 according to the present invention is as follows.

The space-time hyperspectral image processing unit 200 according to the present invention includes two wavelengths 1 (λ ₁ ) and 2 (wavelengths) selected in consideration of the number of all the wavelengths constituting the hyperspectral image. A spectral characteristic extraction unit 200a for extracting spectral characteristics for λ ₂ ), a band ratio calculation unit 200b for calculating a band ratio or band index according to the definition of spectral characteristics, and regression analysis of observed values and spectral characteristics The correlation coefficient calculation unit 200c that calculates the correlation coefficient through the method, and the wavelength wavelength 1 (λ ₁ ) and the wavelength 2 (λ ₂ ) are stored as a color at the position of the ordered pair to construct a correlation coefficient map. It includes a correlation coefficient map construction unit (200d).

A detailed description of a cross-sectional monitoring method using a space-time hyperspectral image according to the present invention is as follows.

3 is a flow chart showing a cross-sectional monitoring method using a space-time hyperspectral image according to the present invention.

First, a hyperspectral image is acquired by a hyperspectral image acquisition device 100 that has a hyperspectral sensor and accumulates and records a line scan area through the position and posture of the drone and the posture of the gimbal to record and provide hyperspectral images. .(S301)

Subsequently, by receiving the hyperspectral image from the hyperspectral image acquisition device 100, learning the correlation between the observed value corresponding to the part of the photographed time existing in the section where the hyperspectral image was collected and the spectral characteristic index of the corresponding location Performs a spatiotemporal hyperspectral image processing step of estimating the change of the observed value of the section with time (S302 ~ S305).

Then, the observation value change analysis result output unit 300 outputs the observation value change analysis result of the spatiotemporal hyperspectral image processing unit 200 (S306).

The spatiotemporal hyperspectral image processing step,

First, with respect to the total number of wavelengths constituting the hyperspectral image, the spectral characteristics of the two wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) are extracted in consideration of the number of all cases in which two wavelengths are selected. (S302)

Then, the band ratio or the band index is calculated according to the definition of the spectral characteristics (S303).

Then, the correlation coefficient is calculated through the regression analysis of the observed values and spectral characteristics (S304).

Then, the correlation coefficient map is constructed by storing the magnitude of the correlation coefficient as a color at the positions of the ordered pairs of the wavelength wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) (S305).

4 and 5 are configuration diagrams showing a hyperspectral image acquisition method according to the present invention.

4 is a mobile platform equipped with a hyperspectral sensor in the drone-based hyperspectral image acquisition system of the present invention, and shows a method of performing a line scan through the position and posture of the drone and the posture of the gimbal using a drone. .

The shooting area of the captured hyperspectral image can be defined as the length of the line projected on the water surface by the viewing angle of the hyperspectral sensor fixed around the drone, and is the same as a set of hyperspectral scanning lines.

5 is an example of a platform on which the present invention can be carried out, showing a system for acquiring a hyperspectral image by installing a structure existing in a river or a separate structure, and mounting a hyperspectral sensor on the platform. It is to directly shoot the section to be monitored.

6 is a block diagram showing a hyperspectral image and location information acquired by a drone-based hyperspectral sensor.

6 is an original image obtained by collecting hyperspectral images based on a drone-based hyperspectral image acquisition system, including a visible light region, a hyperspectral image composed of several bands such as ultraviolet and infrared regions, and a pixel constituting the hyperspectral image, It is an illustration of the latitude and longitude of the location information of the pixel.

7 is a block diagram showing a method of calculating a spectral characteristic for a location where an observation value exists from a hyperspectral image.

7 illustrates a method of calculating spectral characteristics for a location where an observation value exists from a general hyperspectral image. The spectral characteristics are defined in the form of a band ratio as shown in Equation 1 or an exponential form as shown in Equation 2.

는 파장(

)에 대한 빛의 세기이다.here,

Is the wavelength(

) Is the intensity of light.

8 is a block diagram showing a process of constructing a correlation coefficient map.

The method of constructing the correlation coefficient map is for the total number of wavelengths constituting the hyperspectral image, and for the two wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) selected in consideration of the number of all cases in which two wavelengths are selected. 1) Extract the spectral characteristics, 2) calculate the band ratio or band index according to the definition of the spectral characteristics described above, 3) calculate the correlation coefficient through regression analysis of the observed values and spectral characteristics described above, 4) wavelength Wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) are stored at the position of the ordered pair by storing the magnitude of the correlation coefficient as a color.

9 is a block diagram showing a change in physical quantity of a cross section over time by using a relational expression between spectral characteristics and observed values.

9 is an embodiment according to the present invention, in comparison with the use of a general hyperspectral image that two axes constituting data are space, the change in the physical quantity of a section over time according to the present invention is composed of a spatial axis and a time axis. It is a feature.

FIG. 10 is a block diagram showing the spatial distribution of observed values over time, showing the spatial distribution of observed values over time, and it is possible to grasp a change in the observed values of a cross section over time.

11 is a block diagram showing changes in observed values at an arbitrary point over time.

By showing the change of the observed value over time at an arbitrary point, it is possible to grasp the change of the observed value at the same point (Pixel Point in FIG. 10).

The cross-sectional monitoring apparatus and method using a spatiotemporal hyperspectral image according to the present invention described above applies a technology for securing spectral characteristics over various times at the same location by utilizing a spatiotemporal hyperspectral image for cross-sectional monitoring, to a depth adjacent to a river structure. In this case, the change in water depth occurring near the structure can be grasped over time.

In addition, by utilizing the spatiotemporal hyperspectral image for cross-sectional monitoring, the technology of securing the spectral characteristics over various times at the same location is combined with the chemical substance detection technology adjacent to the river structure, so that it is possible to easily identify whether a chemical substance is leaked from the river. do.

As described above, it will be understood that the present invention is implemented in a modified form without departing from the essential characteristics of the present invention.

Therefore, the specified embodiments should be considered from a descriptive point of view rather than a limiting point of view, and the scope of the present invention is indicated in the claims rather than the above description, and all differences within the scope equivalent thereto are included in the present invention. It will have to be interpreted.

100. Hyperspectral image acquisition device
200. Spatiotemporal hyperspectral image processing unit
300. Observed value change analysis result output section

Claims (10)

A hyperspectral image acquisition device including a hyperspectral sensor and recording and providing hyperspectral images;
The hyperspectral image is received from the hyperspectral image acquisition device and the correlation of the observed value corresponding to the part of the photographed time present in the section where the hyperspectral image is collected and the spectral characteristic index of the corresponding location is learned, A space-time hyperspectral image processing unit that estimates a change in the observed value of;
An observation value change analysis result output unit for outputting the observation value change analysis result of the spatiotemporal hyperspectral image processing unit so that the spatial distribution of the observed values over time can be known; a cross-sectional monitoring device using a spatiotemporal hyperspectral image, comprising: . The method of claim 1, wherein the space-time hyperspectral image processing unit,
For the total number of wavelengths constituting the hyperspectral image, extracting spectral characteristics to extract spectral characteristics for two wavelengths, wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ), selected in consideration of the number of all cases in which two wavelengths are selected Wealth,
A band ratio calculation unit that calculates the band ratio or band index according to the definition of the spectral characteristics,
A correlation coefficient calculation unit that calculates a correlation coefficient through regression analysis of observed values and spectral characteristics,
A spatiotemporal hyperspectral image, characterized in that it comprises a correlation coefficient map construction unit for constructing a correlation coefficient map by storing the magnitude of the correlation coefficient as a color at the positions of the ordered pairs of wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ). Section monitoring device used. The method of claim 1, wherein the hyperspectral image acquisition device is configured as a mobile platform using a drone,
It is operated hydraulically in the center of the drone and equipped with a damper that mitigates vibrations or shocks that may occur in the drone or gimbal for movement during flight, and always maintains the same posture at the bottom of the drone so that shooting can take place. Connect the gimbal,
The gimbal includes a hyperspectral sensor that accumulates and records line scan areas through the position and posture of the drone and the posture of the gimbal to record hyperspectral images, and a camera that monitors the drone and shooting status during flight. Sectional monitoring device using spatiotemporal hyperspectral images. The method of claim 3, wherein the photographing area of the hyperspectral image captured by the hyperspectral image acquisition device configured as a mobile platform using a drone,
A cross-sectional monitoring device using spatiotemporal hyperspectral images, characterized in that the viewing angle of a hyperspectral sensor fixed around a drone can be defined as the length of a line projected onto the water surface, and is a set of hyperspectral scanning lines. The method of claim 1, wherein the hyperspectral image provided by the hyperspectral image acquisition device is composed of a plurality of bands including a visible light region, an ultraviolet ray, and an infrared region,
A cross-sectional monitoring device using a spatiotemporal hyperspectral image, characterized in that latitude and longitude are recorded for location information of pixels constituting the hyperspectral image. The method of claim 1, wherein the space-time hyperspectral image processing unit,
A cross-section monitoring device using a spatiotemporal hyperspectral image, characterized in that the change in the physical quantity of a cross section over time is composed of a spatial axis and a time axis by utilizing a relational expression between spectral characteristics and observed values. The method of claim 1, wherein the observation value change analysis result output unit,
A cross-sectional monitoring device using a spatiotemporal hyperspectral image, characterized in that it provides to know the spatial distribution of the observed values over time so that changes in the observed values at the same point can be identified. Acquiring a hyperspectral image by a hyperspectral image acquisition device;
By receiving the hyperspectral image from the hyperspectral image acquisition device and learning the correlation between the observed value corresponding to the part of the photographed time existing in the section where the hyperspectral image was collected and the spectral characteristic index of the location, A spatiotemporal hyperspectral image processing step of estimating a change in observed value;
And outputting the observed value change analysis result to know the spatial distribution of the observed value over time by the observation value change analysis result output unit. The method of claim 8, wherein the spatiotemporal hyperspectral image processing step,
Extracting spectral characteristics for two wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) selected in consideration of the number of all the wavelengths constituting the hyperspectral image, and
Calculating a band ratio or a band index according to the definition of spectral characteristics, and
Calculating a correlation coefficient through regression analysis of the observed values and spectral characteristics,
Sectional monitoring using a spatiotemporal hyperspectral image comprising the step of constructing a correlation coefficient map by storing the magnitude of the correlation coefficient as a color at the positions of the ordered pairs of wavelengths wavelength 1 (λ ₁ ) and wavelength 2 (λ ₂ ) Way. The method of claim 9, wherein in order to calculate the spectral characteristics for the location where the observed value exists from the hyperspectral image

Define the band ratio as

To define the spectral characteristics in exponential form,

Is the wavelength(

Sectional monitoring method using a spatiotemporal hyperspectral image, characterized in that the intensity of light for ).

Images