Three-dimensional atlas splicing method based on hyperspectral imager of unmanned aerial vehicle

Abstract

The invention relates to the technical field of hyperspectral imaging, in particular to a three-dimensional atlas splicing method based on an unmanned aerial vehicle hyperspectral imager, which comprises the following steps: acquiring state parameters of image data acquired by the unmanned aerial vehicle hyperspectral imager, and judging the imaging state to obtain state parameter signals; an imaging state value is calculated based on the state parameters; acquiring regional parameters of image data acquired by the hyperspectral imager of the unmanned aerial vehicle, judging an imaging region to obtain a regional parameter signal, and calculating an imaging regional value based on the regional parameters; performing fusion analysis based on the imaging state value and the imaging area value to obtain an imaging to-be-spliced screening value; obtaining an imaging quality value, and further screening the imaging data to obtain spliced image data; according to the invention, the to-be-spliced image data is obtained through screening, so that the relatively high consistency and coherence of the spliced atlas in vision can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hyperspectral imaging, and in particular to a stereogram splicing method based on a hyperspectral imager of an unmanned aerial vehicle. Background Art

[0002] The stereo map stitching method based on UAV hyperspectral imagers involves several key steps that ensure high-quality map stitching, thereby providing accurate and detailed ground object information. The following are the detailed steps of the method:

[0003] 1. Data Collection

[0004] First, a drone equipped with a hyperspectral imager is used to perform flight operations to collect hyperspectral image data of the target area. During the flight, the drone needs to maintain a stable flight altitude and speed to ensure that the collected image data is consistent and comparable.

[0005] 2. Preprocessing

[0006] The collected raw hyperspectral image data needs to be preprocessed to eliminate noise, correct distortion and improve image quality. The preprocessing steps include radiometric calibration, atmospheric correction, geometric correction, etc. These steps help eliminate the impact of environmental factors on image data and improve the accuracy of subsequent processing.

[0007] 3. Stereo Matching

[0008] Next, stereo matching is performed, which is a key step in atlas stitching. By comparing the image data collected from different perspectives, the corresponding points of the same object are found, thus establishing a stereo correspondence. This step requires the use of computer vision and image processing technology to achieve accurate matching and establish a correspondence.

[0009] 4. Splicing and Fusion

[0010] Based on stereo matching, stitching and fusion are performed. Through a certain algorithm, image data from different perspectives are fused together to form a complete stereo map. In the stitching process, factors such as image color, brightness, and contrast need to be considered to ensure that the stitched map is visually consistent and coherent.

[0011] It can be known that image data collection is the source of the stereoscopic picture stitching process and is also an important link. The quality of image data collection will affect the quality of stereoscopic picture stitching.

[0012] Chinese patent CN110111255B discloses a stereo image stitching method, the method comprising the following steps: inputting left and right stereo images, pre-aligning the left and right images and reducing the vertical disparity through disparity minimization homography; further aligning the left and right images through multi-constraint deformation, while introducing an initial disparity map to maintain the disparity consistency of the stereo image; determining the best seam based on the seam selection and fusion of the disparity consistency and performing stereo image stitching;

[0013] In the prior art, during the acquisition process, the UAV hyperspectral imager cannot progressively screen the obtained image data according to factors such as the image imaging status and quality, which is not conducive to the subsequent stitching of stereograms and cannot effectively improve the quality of stereogram stitching. Summary of the invention

[0014] The purpose of the present invention is to provide a stereo map stitching method based on an unmanned aerial vehicle hyperspectral imager. The technical problem solved by the present invention is that the obtained image data cannot be progressively screened according to factors such as the state and quality of the image during imaging, which is not conducive to the subsequent stitching of stereo maps and cannot effectively improve the quality of stereo map stitching.

[0015] The purpose of the present invention can be achieved through the following technical solutions:

[0016] A stereogram stitching method based on a UAV hyperspectral imager includes: data collection, stitching and fusion. In the data collection process, the following steps are included:

[0017] Step 1: Obtain the state parameters of the image data collected by the UAV hyperspectral imager, judge the state during imaging to obtain the state parameter signal, and then calculate the imaging state value based on the state parameters;

[0018] Among them, the state parameters include hovering position and imaging angle;

[0019] Step 2: Obtain the regional parameters of the image data collected by the UAV hyperspectral imager, and determine the imaging area to obtain the regional parameter signal, and then calculate the imaging area value based on the regional parameters;

[0020] Wherein, the regional parameters include the range of the imaging area;

[0021] Step 3: Perform fusion analysis based on the imaging state value and the imaging area value to obtain the imaging stitching screening value;

[0022] Step 4: Based on the imaging stitching screening value ZSq obtained at each acquisition point of the imaging trajectory, the imaging quality value is obtained, and the imaging data is further screened to obtain the stitching image data.

[0023] As a further solution of the present invention: in step 1, the process of acquiring the state parameter signal is:

[0024] If the hovering position is within the hovering position range, a hovering qualified signal is generated; if the imaging angle is within the imaging angle range, an angle qualified signal is generated;

[0025] If both the hovering qualified signal and the angle qualified signal are obtained at the same time, a high-quality imaging status signal is generated.

[0026] As a further solution of the present invention: the calculation process of the imaging state value is:

[0027] When a high-quality imaging state signal is obtained, a hovering position deviation ratio and an imaging angle deviation ratio are obtained, and the hovering position deviation ratio and the imaging angle deviation ratio are added and summed to obtain an imaging state value;

[0028] Specifically, the process of obtaining the hovering position deviation ratio is as follows:

[0029] Obtaining the distance between the hovering position and the center point of the hovering position range, marking it as the hovering position deviation value, dividing the hovering position deviation value by the hovering position range, and calculating the hovering position deviation ratio;

[0030] The process of obtaining the imaging angle deviation ratio is:

[0031] The obtained imaging angle is calculated as a difference with the middle value of the imaging angle range to obtain an imaging angle offset value, and the imaging angle offset value is divided by the imaging angle range value to calculate the imaging angle deviation ratio.

[0032] As a further solution of the present invention: in step 2, the process of acquiring the regional parameter signal is:

[0033] Compare the overlap between the current imaging area and the previous imaging area to obtain the overlap area;

[0034] If the area of ​​the overlapped region is greater than or equal to the overlapped region area threshold, an imaging region qualified signal is generated.

[0035] As a further solution of the present invention: the process of obtaining the imaging area value is:

[0036] When a qualified imaging area signal is obtained, the imaging area ratio and the imaging area neatness ratio are obtained, and the imaging area ratio is subtracted from the imaging area neatness ratio to obtain an imaging area value;

[0037] The process of obtaining the imaging area ratio is:

[0038] The obtained overlapping area is divided by the overlapping area threshold to obtain the imaging area ratio;

[0039] The process of obtaining the uniformity ratio of the imaging area is:

[0040] Get the endpoints of the overlapping area, connect the endpoints end to end to get the line length of the imaging area, then get the actual boundary line length of the overlapping area, divide the actual boundary line length of the overlapping area by the line length of the imaging area to get the imaging area neatness ratio.

[0041] As a further solution of the present invention: in step 3, the imaging state value and the current imaging area value are obtained and marked as ZZ and ZQ respectively, and the imaging stitching screening value ZSq is calculated by the formula ZSq=a1*ZZ+a2*ZQ, wherein a1 and a2 are both proportional coefficients;

[0042] The image data collected by the hyperspectral imager are screened by the imaging stitching screening value ZSq, and the image data with the larger imaging stitching screening value ZSq is preferentially selected.

[0043] As a further solution of the present invention: in step 4, all image data at each acquisition point of the imaging trajectory are obtained, arranged from large to small according to the imaging stitching screening value ZSq, and a preset preliminary screening number is set, and the image data arranged at the back is deleted to obtain a preliminary screening image;

[0044] Based on the preliminary screening images, the image brightness ratio and the image contrast ratio are obtained, and the image brightness ratio and the image contrast ratio are added and summed to obtain the imaging quality value;

[0045] Arrange the preliminary screening images of each acquisition point from large to small according to the imaging quality value, set a preset preliminary screening quantity, delete the image data at the end of the arrangement, and obtain a re-screened image.

[0046] As a further solution of the present invention: a three-dimensional coordinate system is constructed with the acquisition point position as the X-axis, the imaging stitching screening value as the Y-axis, and the imaging quality value as the Z-axis, and the acquisition point position, the imaging stitching screening value, and the imaging quality value corresponding to the re-screened image are substituted into the three-dimensional coordinate system to obtain the imaging coordinate point;

[0047] By extracting the formula Extract the image data of each acquisition point position that satisfies the extraction formula in the three-dimensional coordinates, thereby obtaining the spliced ​​image data; wherein, in the extraction formula, L i-1 It represents the distance difference between the imaging coordinate points in adjacent acquisition points, D i It represents the distance from the imaging coordinate point in the acquisition point position to the acquisition point on the X axis, that is, y i Represents the y-axis coordinate value of the imaging coordinate point, Z irepresents the z-axis coordinate value of the imaging coordinate point, i represents the number of acquisition point positions, and LY represents all L i-1 The threshold for summing up, DY represents all D i The threshold for summing.

[0048] As a further solution of the present invention: the process of obtaining the image brightness ratio is:

[0049] The obtained image brightness is divided by the image brightness threshold to obtain the image brightness ratio.

[0050] As a further solution of the present invention: the process of obtaining the image contrast ratio is:

[0051] The resulting image contrast is divided by the image contrast threshold to obtain the image contrast ratio.

[0052] Beneficial effects of the present invention:

[0053] (1) In the process of monitoring the collected hyperspectral image data, the present invention obtains the state parameters of the image data collected by the unmanned aerial vehicle hyperspectral imager, judges the state during imaging to obtain the state parameter signal, and then calculates the imaging state abnormal value based on the state parameter; obtains the regional parameters of the image data collected by the unmanned aerial vehicle hyperspectral imager, judges the imaging region to obtain the regional parameter signal, and then calculates the imaging region abnormal value based on the regional parameter; performs fusion analysis based on the imaging state value and the imaging region value to obtain the imaging splicing screening value; the image data collected by the hyperspectral imager imaging can be screened, so that the screened image data is conducive to the subsequent splicing of the stereogram, and improves the quality of the stereogram splicing;

[0054] (2) The present invention obtains an imaging quality value based on the imaging to-be-stitched screening value ZSq obtained at each acquisition point of the imaging trajectory, and further screens the imaging data. The present invention combines the imaging to-be-stitched screening value and the imaging quality value in sequence to further screen the image data to be spliced. The imaging to-be-stitched screening value and the imaging quality value are then combined in three dimensions to further screen the image data to be spliced, thereby effectively improving the visual consistency and coherence of the spliced ​​map. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described below in conjunction with the accompanying drawings.

[0056] Figure 1 is a flowchart of a stereogram splicing method in Embodiment 1 of the present invention;

[0057] Figure 2 is a flowchart of the real-time monitoring process in Example 1 of the present invention;

[0058] Figure 3 It is a flowchart of the real-time monitoring process in Example 2 of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Example 1

[0061] See also Figure 1 As shown, the present invention is a stereogram stitching method based on a UAV hyperspectral imager, comprising the following steps:

[0062] Step 1: Data Collection

[0063] First, a UAV equipped with a hyperspectral imager is used to perform flight operations to collect hyperspectral image data of the target area; the collected hyperspectral image data is monitored in real time;

[0064] Step 2: Splicing and Fusion

[0065] By comparing the image data collected from different perspectives, the corresponding points of the same object are found, thereby establishing a three-dimensional correspondence relationship; the image data from different perspectives are fused together to form a complete three-dimensional map;

[0066] It should be noted that the stitching and fusion method may adopt a stereoscopic image stitching method disclosed in patent document with publication number CN110111255B.

[0067] See also Figure 2 As shown, the real-time monitoring process in step 1 includes the following steps:

[0068] Step 1: Obtain the state parameters of the image data collected by the UAV hyperspectral imager, judge the state during imaging to obtain the state parameter signal, and then calculate the imaging state value based on the state parameters;

[0069] Among them, the state parameters include hovering position and imaging angle;

[0070] In some embodiments, when the UAV hyperspectral imager moves along the imaging trajectory, the hovering position and imaging angle of the UAV hyperspectral imager are obtained;

[0071] Compare the hovering position with the hovering position range, and if the hovering position is within the hovering position range, generate a hovering qualified signal; if the hovering position is not within the hovering position range, generate a hovering unqualified signal;

[0072] Compare the imaging angle with the imaging angle range, and if the imaging angle is within the imaging angle range, generate an angle qualified signal; if the imaging angle is not within the imaging angle range, generate an angle unqualified signal;

[0073] The hovering qualified signal, the hovering unqualified signal, the angle qualified signal and the angle unqualified signal are combined and analyzed, that is, if the hovering qualified signal and the angle qualified signal are obtained at the same time, a high-quality imaging state signal is generated, otherwise (the otherwise situation refers to including one or both of the hovering qualified signal and the angle qualified signal), a low-quality imaging state signal is generated;

[0074] When a high-quality imaging state signal is obtained, a hovering position deviation ratio and an imaging angle deviation ratio are obtained, and the hovering position deviation ratio and the imaging angle deviation ratio are added and summed to obtain an imaging state value;

[0075] Specifically, the process of obtaining the hovering position deviation ratio is as follows:

[0076] Obtaining the distance between the hovering position and the center point of the hovering position range, marking it as the hovering position deviation value, dividing the hovering position deviation value by the hovering position range, and calculating the hovering position deviation ratio;

[0077] The process of obtaining the imaging angle deviation ratio is:

[0078] The obtained imaging angle is calculated as a difference with the middle value of the imaging angle range to obtain an imaging angle offset value, and the imaging angle offset value is divided by the imaging angle range value to calculate an imaging angle deviation ratio;

[0079] It should be explained that the state stability of the hyperspectral imager during imaging can be effectively judged by the degree of deviation of the hovering position and imaging angle during imaging by the hyperspectral imager, that is, the smaller the hovering position deviation ratio and imaging angle deviation ratio are, the higher the quality of the image data collected by the hyperspectral imager.

[0080] Step 2: Obtain the regional parameters of the image data collected by the UAV hyperspectral imager, and determine the imaging area to obtain the regional parameter signal, and then calculate the imaging area value based on the regional parameters;

[0081] Wherein, the regional parameters include the range of the imaging area;

[0082] In some embodiments, when the UAV hyperspectral imager moves along the imaging trajectory, the imaging area range of the UAV hyperspectral imager is obtained;

[0083] Compare the overlap between the current imaging area and the previous imaging area to obtain the overlap area;

[0084] Compare the area of ​​the overlapping region with the threshold of the overlapping region area;

[0085] If the area of ​​the overlapped region is greater than or equal to the overlapped region area threshold, a qualified imaging region signal is generated;

[0086] If the area of ​​the overlapped region is smaller than the overlapped region area threshold, an imaging region unqualified signal is generated;

[0087] When a qualified imaging area signal is obtained, the imaging area ratio and the imaging area neatness ratio are obtained, and the imaging area ratio is subtracted from the imaging area neatness ratio to obtain an imaging area value;

[0088] Specifically, the process of obtaining the imaging area ratio is as follows:

[0089] The obtained overlapping area is divided by the overlapping area threshold to obtain the imaging area ratio;

[0090] The process of obtaining the uniformity ratio of the imaging area is:

[0091] Obtain the endpoints of the overlapped area, connect the endpoints end to end to obtain the line length of the imaging area, then obtain the actual boundary line length of the overlapped area, divide the actual boundary line length of the overlapped area by the line length of the imaging area, and obtain the imaging area neatness ratio;

[0092] It should be explained that the imaging area range is obtained by the adjacent acquisition points when the hyperspectral imager is imaging. The imaging area ratio and the imaging area neatness ratio can be used to effectively judge the continuity of adjacent images during the imaging acquisition of the hyperspectral imager. That is, the larger the overlapping area between adjacent images, the closer the actual boundary of the overlapping area is to a straight line, indicating that the continuity of adjacent images is good, which is convenient for subsequent splicing and overlapping of the images.

[0093] Step 3: Perform fusion analysis based on the imaging state value and the imaging area value to obtain the imaging stitching screening value;

[0094] In some embodiments, an imaging state value and a current imaging area value are obtained and marked as ZZ and ZQ respectively. The imaging to-be-stitched screening value ZSq is calculated by the formula ZSq=a1*ZZ+a2*ZQ, wherein a1 and a2 are both proportional coefficients, a1+a2=1, a1 is 0.62, a2 ​​is 0.38, and the values ​​of a1 and a2 represent the influence of the current imaging state value and the current imaging area value on the current imaging to-be-stitched screening value ZSq respectively.

[0095] It can screen the image data collected by the hyperspectral imager through the imaging screening value ZSq to be stitched, and give priority to image data with a larger imaging screening value ZSq to be stitched, which is conducive to the subsequent stitching of stereograms and improves the quality of stereogram stitching;

[0096] It should be noted that the image data can be easily screened by performing fusion analysis of the imaging state value and the imaging area value;

[0097] The technical solution of the embodiment of the present invention is as follows: in the process of monitoring the collected hyperspectral image data, the state parameters of the image data collected by the unmanned aerial vehicle hyperspectral imager are obtained, and the state during imaging is judged to obtain the state parameter signal, and then the imaging state abnormal value is calculated based on the state parameter; the regional parameters of the image data collected by the unmanned aerial vehicle hyperspectral imager are obtained, and the imaging region is judged to obtain the regional parameter signal, and then the imaging region abnormal value is calculated based on the regional parameter; a fusion analysis is performed based on the imaging state value and the imaging region value to obtain the imaging splicing screening value; the image data collected by the hyperspectral imager can be screened, so that the screened image data is conducive to the subsequent splicing of the stereogram and improves the quality of the stereogram splicing.

[0098] Example 2

[0099] See also Figure 3 As shown, the real-time monitoring process in step 1 also includes the following steps:

[0100] Step 4: Based on the imaging stitching screening value ZSq obtained at each acquisition point of the imaging trajectory, the imaging quality value is obtained, and the imaging data is further screened to obtain the stitching image data;

[0101] In some embodiments, all image data at each acquisition point of the imaging trajectory are obtained, arranged from large to small according to the imaging stitching screening value ZSq, and a preset preliminary screening quantity is set, and the image data arranged at the rear is deleted to obtain a preliminary screening image; preferably, the preset preliminary screening quantity is: 1 / 2 of all image data at each acquisition point;

[0102] Based on the preliminary screening images, the image brightness ratio and the image contrast ratio are obtained, and the image brightness ratio and the image contrast ratio are added and summed to obtain the imaging quality value;

[0103] Arrange the preliminary screening images of each acquisition point from large to small according to the imaging quality value, set a preset preliminary screening quantity, delete the image data at the rear of the arrangement, and obtain a re-screened image; preferably, the preset re-screening quantity is: 1 / 2 of all the image data of each acquisition point;

[0104] With the acquisition point position as the X-axis, the imaging stitching screening value as the Y-axis, and the imaging quality value as the Z-axis, a three-dimensional coordinate system is constructed, and the acquisition point position, the imaging stitching screening value, and the imaging quality value corresponding to the re-screened image are substituted into the three-dimensional coordinate system to obtain the imaging coordinate point;

[0105] By extracting the formula Extract the image data of each acquisition point position that satisfies the extraction formula in the three-dimensional coordinates, thereby obtaining the spliced ​​image data; wherein, in the extraction formula, L i-1 It represents the distance difference between the imaging coordinate points in adjacent acquisition points, D i It represents the distance from the imaging coordinate point in the acquisition point position to the acquisition point on the X axis, that is, y i Represents the y-axis coordinate value of the imaging coordinate point, Z i represents the z-axis coordinate value of the imaging coordinate point, i represents the number of acquisition point positions, and LY represents all L i-1 The threshold for summing up, DY represents all D i The threshold for summation;

[0106] The obtained stitched image data is sent to the stitching and fusion step to complete the stitching of the stitched image data;

[0107] It should be noted that the significance of the extraction formula is: among the numerous re-screened images, the images with small differences in image data performance in adjacent acquisition positions are extracted by the formula less than or equal to LY, and the images with good image data performance among all the re-screened images are extracted by the formula greater than or equal to DY;

[0108] Therefore, the extracted image data coordinates are used as the stitched image, which can effectively improve the visual consistency and coherence of the stitched image.

[0109] Specifically, the process of obtaining the image brightness ratio is:

[0110] Divide the obtained image brightness by the image brightness threshold to obtain the image brightness ratio;

[0111] The process of obtaining the image contrast ratio is:

[0112] The obtained image contrast is divided by the image contrast threshold to obtain the image contrast ratio. The image contrast refers to the measurement of different brightness levels between the brightest white and the darkest black in the light and dark areas of an image, reflecting the magnitude of the image grayscale contrast;

[0113] The technical solution of the embodiment of the present invention is as follows: based on the imaging to-be-stitched screening value ZSq obtained at each acquisition point of the imaging trajectory, an imaging quality value is obtained, and the imaging data is further screened. The present invention combines the imaging to-be-stitched screening value with the imaging quality value in sequence to further screen the image data to be spliced, and then combines the imaging to-be-stitched screening value with the imaging quality value in three dimensions to further screen the image data to be spliced, thereby effectively improving the visual consistency and coherence of the spliced ​​map.

[0114] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0115] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for stitching stereograms based on a UAV hyperspectral imager, comprising the following steps: Data collection, splicing and fusion, characterized in that the data collection process includes the following steps: Step 1: Obtain the state parameters of the image data collected by the UAV hyperspectral imager, judge the state during imaging to obtain the state parameter signal, and then calculate the imaging state value based on the state parameters; Among them, the state parameters include hovering position and imaging angle; Step 2: Obtain the regional parameters of the image data collected by the UAV hyperspectral imager, and determine the imaging area to obtain the regional parameter signal, and then calculate the imaging area value based on the regional parameters; Wherein, the regional parameters include the range of the imaging area; Step 3: Perform fusion analysis based on the imaging state value and the imaging area value to obtain the imaging stitching screening value; Step 4: Based on the imaging stitching screening value obtained at each acquisition point of the imaging trajectory, the imaging quality value is obtained, and the imaging data is further screened to obtain the stitching image data.

2. The method for stitching stereograms based on a UAV hyperspectral imager according to claim 1 is characterized in that: In step 1, the process of acquiring the state parameter signal is: If the hovering position is within the hovering position range, a hovering qualified signal is generated; if the imaging angle is within the imaging angle range, an angle qualified signal is generated; If both the hovering qualified signal and the angle qualified signal are obtained at the same time, a high-quality imaging status signal is generated.

3. The method for stitching stereograms based on a UAV hyperspectral imager according to claim 2 is characterized in that: The calculation process of the imaging state value is: When a high-quality imaging state signal is obtained, a hovering position deviation ratio and an imaging angle deviation ratio are obtained, and the hovering position deviation ratio and the imaging angle deviation ratio are added and summed to obtain an imaging state value; Specifically, the process of obtaining the hovering position deviation ratio is as follows: Obtaining the distance between the hovering position and the center point of the hovering position range, marking it as the hovering position deviation value, dividing the hovering position deviation value by the hovering position range, and calculating the hovering position deviation ratio; The process of obtaining the imaging angle deviation ratio is: The obtained imaging angle is calculated as a difference with the middle value of the imaging angle range to obtain an imaging angle offset value, and the imaging angle offset value is divided by the imaging angle range value to calculate the imaging angle deviation ratio.

4. The method for stitching stereograms based on a UAV hyperspectral imager according to claim 1 is characterized in that: In step 2, the process of obtaining the regional parameter signal is as follows: Compare the overlap between the current imaging area and the previous imaging area to obtain the overlap area; If the area of ​​the overlapped region is greater than or equal to the overlapped region area threshold, an imaging region qualified signal is generated.

5. The method for stitching stereograms based on a drone hyperspectral imager according to claim 4 is characterized in that: The process of obtaining the imaging area value is as follows: When a qualified imaging area signal is obtained, the imaging area ratio and the imaging area neatness ratio are obtained, and the imaging area ratio is subtracted from the imaging area neatness ratio to obtain an imaging area value; The process of obtaining the imaging area ratio is: The obtained overlapping area is divided by the overlapping area threshold to obtain the imaging area ratio; The process of obtaining the uniformity ratio of the imaging area is: Get the endpoints of the overlapping area, connect the endpoints end to end to get the line length of the imaging area, then get the actual boundary line length of the overlapping area, divide the actual boundary line length of the overlapping area by the line length of the imaging area to get the imaging area neatness ratio.

6. The method for stitching stereograms based on a UAV hyperspectral imager according to claim 1 is characterized in that: In step 3, the imaging state value and the current imaging area value are obtained and marked as ZZ and ZQ respectively. The imaging stitching screening value ZSq is calculated by the formula ZSq=a1*ZZ+a2*ZQ, where a1 and a2 are both proportional coefficients; The image data collected by the hyperspectral imager are screened by the imaging stitching screening value ZSq, and the image data with the larger imaging stitching screening value ZSq is preferentially selected.

7. The method for stitching stereograms based on a UAV hyperspectral imager according to claim 1 is characterized in that: In step 4, all image data at each acquisition point of the imaging trajectory are obtained, arranged from large to small according to the imaging stitching screening value ZSq, and a preset preliminary screening number is set, and the image data arranged at the back is deleted to obtain a preliminary screening image; Based on the preliminary screening images, the image brightness ratio and the image contrast ratio are obtained, and the image brightness ratio and the image contrast ratio are added and summed to obtain the imaging quality value; Arrange the preliminary screening images of each acquisition point from large to small according to the imaging quality value, set a preset preliminary screening quantity, delete the image data at the end of the arrangement, and obtain a re-screened image.

8. The method for stitching stereograms based on a drone hyperspectral imager according to claim 7 is characterized in that: With the acquisition point position as the X-axis, the imaging stitching screening value as the Y-axis, and the imaging quality value as the Z-axis, a three-dimensional coordinate system is constructed, and the acquisition point position, the imaging stitching screening value, and the imaging quality value corresponding to the re-screened image are substituted into the three-dimensional coordinate system to obtain the imaging coordinate point; By extracting the formula Extracting image data of each acquisition point position satisfying the extraction formula in the three-dimensional coordinates, thereby obtaining spliced ​​image data; Among them, in the extraction formula, L i-1 It represents the distance difference between the imaging coordinate points in adjacent acquisition points, D i It represents the distance from the imaging coordinate point in the acquisition point position to the acquisition point on the X axis, that is, y i Represents the y-axis coordinate value of the imaging coordinate point, Z i represents the z-axis coordinate value of the imaging coordinate point, i represents the number of acquisition point positions, and LY represents all L i-1 The threshold for summing up, DY represents all D i The threshold for summing.

9. The method for stitching stereograms based on a drone hyperspectral imager according to claim 8, characterized in that: The process of obtaining the image brightness ratio is: The obtained image brightness is divided by the image brightness threshold to obtain the image brightness ratio.

10. The method for stitching stereograms based on a drone hyperspectral imager according to claim 8, characterized in that: The process of obtaining the image contrast ratio is: The resulting image contrast is divided by the image contrast threshold to obtain the image contrast ratio.

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