CN108225318B - Image quality-based aviation remote sensing path planning method and system - Google Patents
Image quality-based aviation remote sensing path planning method and system Download PDFInfo
- Publication number
- CN108225318B CN108225318B CN201711227820.4A CN201711227820A CN108225318B CN 108225318 B CN108225318 B CN 108225318B CN 201711227820 A CN201711227820 A CN 201711227820A CN 108225318 B CN108225318 B CN 108225318B
- Authority
- CN
- China
- Prior art keywords
- image
- remote sensing
- image quality
- aerial
- route
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
Abstract
The invention provides an aerial remote sensing path planning method based on image quality. The image quality-based aviation remote sensing path planning method comprises the following steps: firstly, analyzing the image quality and the image area of a required remote sensing image in advance; secondly, calculating the flight state of the aircraft according to the image quality and the area of the image area; and thirdly, determining an air route and an air route of aerial remote sensing according to the flight state of the aircraft. The invention also provides a system of the aerial remote sensing path planning method based on the image quality.
Description
Technical Field
The invention relates to the technical field of aerial remote sensing, in particular to an aerial remote sensing path planning method and system based on image quality.
Background
Currently, with the development of aerial remote sensing technology, the aerial remote sensing technology has played more and more roles in modern life, such as disaster assessment, farmland area prediction, farmland aerial pesticide spraying and the like.
However, in the conventional aerial remote sensing technology, the focus is on how to acquire a high-quality remote sensing image, but the focus is less on the remote sensing route of the aircraft. This results in that in the remote sensing mapping, in order to obtain comprehensive image data, the aircraft may be repeatedly subjected to coverage shooting, resulting in low working efficiency of the remote sensing mapping.
For example, in the field of farmland remote sensing, in order to obtain a remote sensing picture with appropriate image quality and calculate a reasonable vegetation index, generally, a high-quality remote sensing image is obtained, and then the vegetation coefficient is calculated, but the factors of flight height and the like are rarely considered, so that the remote sensing operation efficiency is not high.
Therefore, in order to improve the working efficiency and the working quality of aerial remote sensing, it is necessary to provide an aerial remote sensing path planning method and system based on image quality.
Disclosure of Invention
The invention aims to provide an image quality-based aerial remote sensing path planning method and system capable of improving the working efficiency and the working quality of aerial remote sensing.
The technical scheme of the invention is as follows: an aerial remote sensing path planning method based on image quality comprises the following steps: the method comprises the steps of firstly, analyzing the image quality and the image area of a required remote sensing image in advance, and analyzing the influence of different image qualities on the collection of vegetation indexes such as NDVI, RVI, GVI and SAVI in advance; secondly, calculating the flight state of the aircraft according to the vegetation index required to be acquired by remote sensing, the image quality and the image area; and thirdly, determining an air route and an air route of aerial remote sensing according to the flight state of the aircraft.
Preferably, the step one specifically includes the steps of: presetting the image quality of a required remote sensing image, wherein the image quality comprises the following technical indexes: image definition, signal-to-noise ratio, gray level mean square error, gradient mean square error, mixed entropy and radiation precision; and carrying out regional point pixel analysis on the remote sensing images with different heights, analyzing the pixel value between two points of the images, and comprehensively calculating the image regional area of the aerial image.
Preferably, the second step specifically includes the following steps: calculating the flight height of the aircraft according to the vegetation index required to be obtained and the image quality of the remote sensing image; and calculating the flight speed, the continuous aerial photographing time and the air route width of the aircraft according to the image area of the remote sensing image.
Preferably, the step of calculating the flight altitude of the aircraft according to the vegetation index to be acquired and the image quality of the remote sensing image specifically comprises the following steps: analyzing the relationship between each technical index in the image quality and the flight height by utilizing a trigonometric function fitting method; and acquiring the plant protection index type to be acquired at this time, and performing weighted fusion on the technical indexes of the image quality by using Kalman filtering and weighted average algorithm to determine the optimal flight height.
Preferably, in step three, the determination of the route and route of aerial remote sensing comprises: the method comprises the following steps of optimizing a shortest flight route, optimizing a minimum turning frequency, optimizing an optimal flight speed and height and optimizing an aerial photography control point.
The system for planning the aerial remote sensing path based on the image quality comprises the following steps: the image analysis module is used for analyzing the image quality and the image area of the required remote sensing image in advance, and analyzing the influence of different image qualities on NDVI, RVI, GVI and SAVI vegetation index acquisition in advance; the flight state determining module is used for calculating the flight state of the aircraft according to the image quality and the image area; and the airway and route determining module is used for determining an airway and a route of aerial remote sensing according to the flight state of the aircraft.
Preferably, in the image analysis module, the image quality of the required remote sensing image is preset, and the image quality includes the following technical indexes: image definition, signal-to-noise ratio, gray level mean square error, gradient mean square error, mixed entropy and radiation precision; carrying out regional point pixel analysis on remote sensing images with different heights, analyzing pixel values between two points of the images, and comprehensively calculating the image regional area of the aerial image; the impact of different image quality on NDVI, RVI, GVI and SAVI vegetation index acquisitions was analyzed.
Preferably, in the flight state determination module, the flight height of the aircraft is calculated according to the vegetation index to be acquired and the image quality of the remote sensing image; and calculating the flight speed, the continuous aerial photographing time and the air route width of the aircraft according to the image area of the remote sensing image.
Preferably, the relationship between the image quality and the flying height is analyzed by a trigonometric function fitting method; and performing weighted fusion on the technical indexes of the image quality by using Kalman filtering and weighted average algorithm to determine the optimal flight height.
Preferably, in the route and route determination module, the determination of the route and route of aerial remote sensing comprises: the method comprises the following steps of optimizing a shortest flight route, optimizing a minimum turning frequency, optimizing an optimal flight speed and height and optimizing an aerial photography control point.
The invention has the beneficial effects that: the image quality-based aerial remote sensing path planning method and system design an aircraft remote sensing airway planning system according to the specific requirements of aerial remote sensing on the remote sensing image quality, optimize the airway and the aerial photography control point on the premise of meeting the image quality, and improve the aerial remote sensing operation efficiency and the operation quality.
Drawings
FIG. 1 is a schematic flow chart of an aerial remote sensing path planning method based on image quality provided by an embodiment of the invention;
FIG. 2 is a block flow diagram of the image quality based aerial remote sensing path planning method shown in FIG. 1;
FIG. 3 is a diagram of remote sensing image contrast for image quality at different elevations;
FIG. 4 is a block diagram of a system according to the image quality-based aerial remote sensing path planning method shown in FIG. 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Unless the context clearly dictates otherwise, the elements and components of the present invention may be present in either single or in multiple forms and are not limited thereto. Although the steps in the present invention are arranged by using reference numbers, the order of the steps is not limited, and the relative order of the steps can be adjusted unless the order of the steps is explicitly stated or other steps are required for the execution of a certain step. It is to be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1 and fig. 2, the image quality-based aviation remote sensing path planning method specifically includes the following steps:
and S1, analyzing the image quality and the image area of the required remote sensing image in advance, and analyzing the influence of different image qualities on NDVI, RVI, GVI and SAVI vegetation index acquisition in advance.
Specifically, step S1 specifically includes the following steps:
presetting the image quality of a required remote sensing image, wherein the image quality comprises the following technical indexes: imaging resolution, image definition, signal-to-noise ratio, modulation transfer function, gray level mean square error, gradient mean square error, mixed entropy and radiation precision;
carrying out regional point pixel analysis on remote sensing images with different heights, analyzing pixel values between two points of the images, and comprehensively calculating the image regional area of the aerial image;
the effect of different image quality on NDVI, RVI, GVI and SAVI vegetation index acquisition was analyzed in advance.
Specifically, in the step of analyzing the influences of different image qualities on the acquisition of the vegetation indexes such as NDVI, RVI, GVI, SAVI, etc. in advance, the influences of different image qualities on the acquisition of the vegetation indexes such as NDVI, RVI, GVI, SAVI, etc. are analyzed, for example, the influence of the image signal-to-noise ratio and the texture on NDVI, RVI, and the influence of the image definition on GVI are large, and the kalman filter and the weighted average algorithm are used for performing weighted fusion on the technical indexes of the image qualities.
As another example, as shown in FIG. 3, photographs of remote sensing images with different image qualities can be obtained at different heights of 0-100 m.
Moreover, the imaging resolution used for analyzing the image quality is one of the most common indicators for evaluating the quality of spectral images, and represents the ability of a certain optical system to express the contrast of tiny details. The resolution board for testing the resolution of the spectrum camera can be carved into stripes with alternate black and white and equal width, and the transmittance of the resolution board changes according to the rectangular wave rule. The method is designed according to the MIL-STD-150A contrast specification of the US army standard, different resolution units of the method use corresponding group and unit marks, the resolution corresponding to each unit of the method can be obtained by looking up a table, and the resolution method has the advantages of single index and convenience in measurement.
The definition represents the definition of the image boundary and can be expressed by the weighted average of the gray scale change rates of adjacent pixels in two directions of X, Y, the definition value has no absolute significance and is only used as an index for mutual comparison, and the definition has great difference due to different ground object types.
Moreover, the sharpness calculates the sharpness (EAV) using an edge sharpness value "a posteriori modeling algorithm":
wherein df/dx is the gray-scale change rate perpendicular to the edge, and f (b) -f (a) is the total contrast in that direction.
The Modulation Transfer Function (MTF) is defined as the ratio of the contrast of the output image to the input image, with a larger MTF indicating a better imaging quality of the optical system.
Furthermore, the modulation transfer function is: t (f) ═ Mi(f)/Mb(f)，
Wherein M isb(f) And Mi(f) Respectively the modulation degree of the ground real of the target object and the modulation degree of the pixel on the corresponding image under the condition of the frequency f,
M＝(u-1)/(u+1)，u＝(ro-rb)/rb(ro，rb) And M represents the radiance of the target and the background.
The signal-to-noise ratio is equal to the ratio of the power spectrums of the signals and the noise, the anti-interference capacity of the imaging system is reflected, and the image quality is cleaner and free of noise points when the signal-to-noise ratio is larger. Wherein a signal-to-noise ratio (SNR) is calculated using the local mean to variance ratio:
wherein M is the average value of the gray scale of the whole image, LSDmAnd the local variance maximum value after the image is partitioned.
The radiation precision is an index reflecting the richness of the image information amount. If the gray distribution range of different images in the same area is larger and the variance is larger, the image information is richer.
The mean m ═ Σ (i × p (i)), and the variance d ═ Σ ((i-m)2X p (i)), where i is the gray value of the image element, and p (i) is the probability that the gray value of the image element is i.
S2, calculating the flight state of the aircraft according to the vegetation index required to be acquired by remote sensing, the image quality and the image area.
Specifically, step S2 specifically includes the following steps:
calculating the flight height of the aircraft according to the vegetation index required to be obtained and the image quality of the remote sensing image;
and calculating the flight speed, the continuous aerial photographing time and the air route width of the aircraft according to the image area of the remote sensing image.
The step of calculating the flight altitude of the aircraft according to the vegetation index to be acquired and the image quality of the remote sensing image specifically comprises the following steps:
analyzing the relationship between each technical index in the image quality and the flight height by utilizing a trigonometric function fitting method;
and acquiring the plant protection index type to be acquired at this time, and performing weighted fusion on the technical indexes of the image quality by using Kalman filtering and weighted average algorithm to determine the optimal flight height.
It should be understood that the brightness of the light needs to be taken into account while taking the image quality factor into account. For example, in a time period with good light, a higher shooting may be selected to improve the work efficiency while ensuring the image quality; in a time period with dark light, lower shooting can be selected, and the image quality needs to be ensured while the work efficiency is ensured.
For example, the trigonometric function fitting formula of the technical indicator of image quality is as follows:
signal-to-noise ratio:
y＝(464.1×sin(0.02494×x-0.02272)+52.85×sin(0.032×x+2.657)+46.28×sin(0.1796×x+1.087))×10/45；
definition:
y＝(2.323×sin(0.02547×x+0.1416)+1.474×sin(0.03476×x+2.756)+0.05419×sin(0.07367×x+3.847))×100/0.9156；
mean square error of gray scale:
y＝(66.08×sin(0.00162×x+0.1521)+2.929×sin(0.1758×x-1.254)+8.25×sin(0.0894×x-3.277))×100/21.9；
mean square error of gradient:
y＝(4.306×sin(0.01249×x+1.474)+1.402×sin(0.02798×x+4.346)+0.2587×sin(0.1328×x+0.6897))×100/3.29；
mixing entropy:
y＝(3.334×sin(0.005064×x+0.4549)+0.4497×sin(0.02674×x+2.464)+0.1884×sin(0.09063×x+1.539))×100/2.2。
and S3, determining the route and the route of aerial remote sensing according to the flight state of the aircraft.
Specifically, in step S3, the determining of the route and route of aerial remote sensing includes: the method comprises the following steps of optimizing a shortest flight route, optimizing a minimum turning frequency, optimizing an optimal flight speed and height and optimizing an aerial photography control point.
For example, in the present embodiment, the full coverage path planning method is basically divided into a foldback type and a spiral type. Specifically, there are methods developed by the shuttle method, the loop method, and the like. Because the aircraft such as the unmanned aerial vehicle has the hovering characteristic, software adopts a common path planning turning-back method, hovering, turning and turning are turned around at the boundary of the operation area, and in practice, the software can be properly selected according to a specific operation shape, the performance of the unmanned aerial vehicle, the turning radius, the turning mode and the like.
Referring to fig. 4, a system for planning an aerial remote sensing path based on image quality according to the method shown in fig. 1 includes: an image analysis module 10, a flight status determination module 20, and a route and course determination module 30.
The image analysis module 10 is configured to analyze image quality and an image area of a desired remote sensing image in advance, and analyze influences of different image qualities on NDVI, RVI, GVI, and SAVI vegetation index acquisition; the flight state determining module 20 calculates the flight state of the aircraft according to the image quality and the image area; the route and route determining module 30 is used for determining the route and route of aerial remote sensing according to the flight state of the aircraft.
Specifically, in the image analysis module 10, firstly, the image quality of the required remote sensing image is preset, and the image quality includes the following technical indexes: image definition, signal-to-noise ratio, gray level mean square error, gradient mean square error, mixed entropy and radiation precision; carrying out regional point pixel analysis on remote sensing images with different heights, analyzing pixel values between two points of the images, and comprehensively calculating the image regional area of the aerial image; moreover, the impact of different image quality on NDVI, RVI, GVI and SAVI vegetation index acquisitions was also analyzed.
In the flight state determination module 20, firstly, the flight height of the aircraft is calculated according to the vegetation index to be acquired and the image quality of the remote sensing image; and then, calculating the flight speed, continuous aerial photographing time and course width of the aircraft according to the image area of the remote sensing image.
Furthermore, in the flight status determination module 20, the relationship between the image quality and the flight height is analyzed by a trigonometric function fitting method; and acquiring the plant protection index type required to be acquired at this time, and performing weighted fusion on the technical indexes of the image quality by using Kalman filtering and weighted average algorithm to determine the optimal flight height.
In the route and route determination module 30, the determination of the route and route of the aerial remote sensing comprises: the method comprises the following steps of optimizing a shortest flight route, optimizing a minimum turning frequency, optimizing an optimal flight speed and height and optimizing an aerial photography control point.
Compared with the prior art, the image quality-based aerial remote sensing path planning method and system provided by the invention have the advantages that the aircraft remote sensing path planning system is designed according to the specific requirements of aerial remote sensing on the quality of remote sensing images, the air route and the aerial photography control point are optimized on the premise of meeting the image quality, and the aerial remote sensing operation efficiency and the operation quality are improved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (9)
1. An aerial remote sensing path planning method based on image quality is characterized in that: the method comprises the following steps:
firstly, analyzing the image quality and the image area of a required remote sensing image in advance, and analyzing the influence of different image qualities on NDVI, RVI, GVI and SAVI vegetation index acquisition in advance;
secondly, calculating the flight state of the aircraft according to the vegetation index required to be acquired by remote sensing, the image quality and the image area; the method comprises the following steps of calculating the flying height of the aircraft according to the vegetation index required to be obtained and the image quality of the remote sensing image, and specifically comprises the following steps:
analyzing the relationship between each technical index in the image quality and the flight height by utilizing a trigonometric function fitting method;
acquiring the type of the plant protection index required to be acquired at this time, and performing weighted fusion on the technical indexes of the image quality by using Kalman filtering and weighted average algorithm to determine the optimal flight height;
and thirdly, determining an air route and an air route of aerial remote sensing according to the flight state of the aircraft.
2. The image quality-based aerial remote sensing path planning method according to claim 1, wherein: the first step specifically comprises the following steps:
presetting the image quality of a required remote sensing image, wherein the image quality comprises the following technical indexes: image definition, signal-to-noise ratio, gray level mean square error, gradient mean square error, mixed entropy and radiation precision;
and carrying out regional point pixel analysis on the remote sensing images with different heights, analyzing the pixel value between two points of the images, and comprehensively calculating the image regional area of the aerial image.
3. The image quality-based aerial remote sensing path planning method according to claim 1, wherein: the second step further comprises:
and calculating the flight speed, the continuous aerial photographing time and the air route width of the aircraft according to the image area of the remote sensing image.
4. The image quality-based aerial remote sensing path planning method according to claim 1, wherein: in step three, the determination of the route and route of aerial remote sensing comprises the following steps: the method comprises the following steps of optimizing a shortest flight route, optimizing a minimum turning frequency, optimizing an optimal flight speed and height and optimizing an aerial photography control point.
5. The system of the image quality-based aerial remote sensing path planning method according to claim 1, wherein: the method comprises the following steps:
the image analysis module is used for analyzing the image quality and the image area of the required remote sensing image in advance, and analyzing the influence of different image qualities on NDVI, RVI, GVI and SAVI vegetation index acquisition in advance;
the flight state determining module is used for calculating the flight state of the aircraft according to the vegetation index required to be acquired by remote sensing, the image quality and the image area;
and the airway and route determining module is used for determining an airway and a route of aerial remote sensing according to the flight state of the aircraft.
6. The system of claim 5, wherein: in the image analysis module, the image quality of the required remote sensing image is preset, and the image quality comprises the following technical indexes: image definition, signal-to-noise ratio, gray level mean square error, gradient mean square error, mixed entropy and radiation precision;
carrying out regional point pixel analysis on remote sensing images with different heights, analyzing pixel values between two points of the images, and comprehensively calculating the image regional area of the aerial image;
the impact of different image quality on NDVI, RVI, GVI and SAVI vegetation index acquisitions was analyzed.
7. The system of claim 5, wherein: and in the flight state determination module, calculating the flight height of the aircraft according to the vegetation index required to be acquired and the image quality of the remote sensing image.
8. The system of claim 5, wherein: analyzing the relation between the image quality and the flying height by a trigonometric function fitting method; and performing weighted fusion on the technical indexes of the image quality by using Kalman filtering and weighted average algorithm to determine the optimal flight height.
9. The system of claim 5, wherein: in the route and route determination module, the determination of the route and route of the aerial remote sensing comprises: the method comprises the following steps of optimizing a shortest flight route, optimizing a minimum turning frequency, optimizing an optimal flight speed and height and optimizing an aerial photography control point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711227820.4A CN108225318B (en) | 2017-11-29 | 2017-11-29 | Image quality-based aviation remote sensing path planning method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711227820.4A CN108225318B (en) | 2017-11-29 | 2017-11-29 | Image quality-based aviation remote sensing path planning method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108225318A CN108225318A (en) | 2018-06-29 |
| CN108225318B true CN108225318B (en) | 2021-11-02 |
Family
ID=62652924
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711227820.4A Active CN108225318B (en) | 2017-11-29 | 2017-11-29 | Image quality-based aviation remote sensing path planning method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108225318B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109099925B (en) * | 2018-08-31 | 2021-07-20 | 江苏大学 | Unmanned agricultural machine navigation path planning and operation quality evaluation method and device |
| CN110503244B (en) * | 2019-07-29 | 2022-04-15 | 武汉大学 | Enteromorpha disaster coverage area-oriented networking optimization monitoring method adopting multiple observation means |
| WO2022016348A1 (en) * | 2020-07-21 | 2022-01-27 | 深圳市大疆创新科技有限公司 | Device control method and apparatus, and computer-readable storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006051031A1 (en) * | 2004-11-10 | 2006-05-18 | Thales | Device for cartographically representing minimum vertical speeds |
| CN101073693A (en) * | 2006-05-16 | 2007-11-21 | 爱斯泰克（上海）高频通讯技术有限公司 | Quasi-realtime forest fire monitoring system based on small aircraft |
| CN104596516A (en) * | 2014-11-24 | 2015-05-06 | 中国海洋大学 | Unmanned aerial vehicle coverage flight path planning based on dynamic newly-added adjacent area |
| WO2015140496A1 (en) * | 2014-03-18 | 2015-09-24 | Bae Systems Plc | Path planning |
| CN105004337A (en) * | 2015-08-19 | 2015-10-28 | 青岛科技大学 | Straight line matching based autonomous navigation method for agricultural unmanned aerial vehicle |
| CN105892483A (en) * | 2016-04-05 | 2016-08-24 | 中科九度(北京)空间信息技术有限责任公司 | Miniature SAR remote sensing observation method based on multi-rotor unmanned aerial vehicle |
| CN106485655A (en) * | 2015-09-01 | 2017-03-08 | 张长隆 | A kind of taken photo by plane map generation system and method based on quadrotor |
| CN106919178A (en) * | 2017-04-14 | 2017-07-04 | 南京信息工程大学 | A kind of plant protection unmanned plane autonomous flight Path Optimize Installation and its optimization method |
-
2017
- 2017-11-29 CN CN201711227820.4A patent/CN108225318B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006051031A1 (en) * | 2004-11-10 | 2006-05-18 | Thales | Device for cartographically representing minimum vertical speeds |
| CN101073693A (en) * | 2006-05-16 | 2007-11-21 | 爱斯泰克（上海）高频通讯技术有限公司 | Quasi-realtime forest fire monitoring system based on small aircraft |
| WO2015140496A1 (en) * | 2014-03-18 | 2015-09-24 | Bae Systems Plc | Path planning |
| CN104596516A (en) * | 2014-11-24 | 2015-05-06 | 中国海洋大学 | Unmanned aerial vehicle coverage flight path planning based on dynamic newly-added adjacent area |
| CN105004337A (en) * | 2015-08-19 | 2015-10-28 | 青岛科技大学 | Straight line matching based autonomous navigation method for agricultural unmanned aerial vehicle |
| CN106485655A (en) * | 2015-09-01 | 2017-03-08 | 张长隆 | A kind of taken photo by plane map generation system and method based on quadrotor |
| CN105892483A (en) * | 2016-04-05 | 2016-08-24 | 中科九度(北京)空间信息技术有限责任公司 | Miniature SAR remote sensing observation method based on multi-rotor unmanned aerial vehicle |
| CN106919178A (en) * | 2017-04-14 | 2017-07-04 | 南京信息工程大学 | A kind of plant protection unmanned plane autonomous flight Path Optimize Installation and its optimization method |
Non-Patent Citations (1)
| Title |
|---|
| 我国航空植保技术的发展前景;薛新宇，等;《中国农机化》;20081231(第5期);正文第72-74 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108225318A (en) | 2018-06-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106896069B (en) | A kind of spectrum reconstruction method based on color digital camera single width RGB image | |
| US8019117B2 (en) | Method and apparatus for evaluating solar radiation amount | |
| US8509476B2 (en) | Automated system and method for optical cloud shadow detection over water | |
| CN104809707A (en) | Method for estimating visibility of single fog-degraded image | |
| CN108225318B (en) | Image quality-based aviation remote sensing path planning method and system | |
| US20140267250A1 (en) | Method and apparatus for digital elevation model systematic error correction and fusion | |
| CN104008543A (en) | Image fusion quality evaluation method | |
| CN108898070A (en) | A kind of high-spectrum remote-sensing extraction Mikania micrantha device and method based on unmanned aerial vehicle platform | |
| Wierzbicki et al. | A method for dehazing images obtained from low altitudes during high-pressure fronts | |
| CN108416091A (en) | A kind of measurement method of easy camera ground resolution and drone flying height relationship | |
| CN104813217B (en) | For the method for the passive single channel imager for being designed to estimate the depth of field | |
| Varjo et al. | Image based visibility estimation during day and night | |
| CN116664448B (en) | Medium-high visibility calculation method and system based on image defogging | |
| CN116994012A (en) | Map spot matching system and method based on ecological restoration | |
| CN115601471B (en) | Drawing method based on large unmanned aerial vehicle photoelectric reconnaissance swept area | |
| EP4078509A1 (en) | Information weighted rendering of 3d point set | |
| CN116721344A (en) | Vegetation detection method, device and equipment based on aerial photographing equipment | |
| Senn et al. | On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys | |
| CN109783973A (en) | A kind of atmospheric visibility calculation method based on image degradation model | |
| CN115272876A (en) | Remote sensing image ship target detection method based on deep learning | |
| Altena et al. | Assessing UAV platform types and optical sensor specifications | |
| Liu et al. | High-spatial-resolution nighttime light dataset acquisition based on volunteered passenger aircraft remote sensing | |
| CN114565733A (en) | Method and device for measuring coverage degree of planting, electronic equipment and medium | |
| WO2016189853A1 (en) | Image processing device, image processing system, image processing method, and program recording medium | |
| JP6856066B2 (en) | Information processing equipment, information processing systems, information processing methods and computer programs |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: No. 100, Liuying, Zhongshan Gate, Xuanwu District, Nanjing, Jiangsu Province, 210000 Applicant after: NANJING Research Institute FOR AGRICULTURAL MECHANIZATION MINISTRY OF AGRICULTURE Address before: No. 100, Liuying, Zhongshan Gate, Xuanwu District, Nanjing, Jiangsu Province, 210000 Applicant before: NANJING Research Institute FOR AGRICULTURAL MECHANIZATION MINISTRY OF AGRICULTURE |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |