Multi-slit hyperspectral imaging system and method based on unmanned aerial vehicle

Abstract

The invention belongs to the technical field of spectral imaging, and relates to a multi-slit hyperspectral imaging system and method based on an unmanned aerial vehicle. The multi-slit assembly comprises a plurality of slits and is used for sampling the output image surface of the front objective lens by using the plurality of slits to obtain a plurality of sampling strips corresponding to the number of the slits; the light splitting and imaging assembly specifically comprises a collimating lens group which is used for collimating the plurality of sampling strips output by the multi-slit assembly; the dispersion prism group comprises a first amexi prism and a second amexi prism and is used for carrying out dispersion light splitting on the plurality of sampling strips output by the collimating lens group; the focusing lens group is used for respectively carrying out focusing imaging on the plurality of sampling strips which are output by the dispersing prism group and are subjected to dispersion light splitting to obtain a plurality of pieces of spectral data; the detector is used for fusing the plurality of spectral data with the spatial information to obtain a plurality of spectral images; and the upper computer is used for fusing the plurality of spectral images to obtain a target spectral image.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral imaging, and in particular to a multi-slit hyperspectral imaging system and method based on an unmanned aerial vehicle. Background Art

[0002] Hyperspectral imaging technology is a combination of imaging technology and spectral technology. It is used to detect the two-dimensional geometric space and one-dimensional spectral information of the target, and obtain continuous, narrow-band image data with high spectral resolution. It has been widely used in agriculture, forestry, ocean, meteorology, environment and military fields.

[0003] The current hyperspectral imaging technology is mainly divided into two categories: snapshot and push-broom. Snapshot hyperspectral imaging technology uses the detector to obtain a complete map data cube of the target area within a single frame exposure. The basic imaging idea is to use the optical system to project or separate the target map three-dimensional data cube and then flatten it on the two-dimensional detector array, and then restore the collected original two-dimensional data to the three-dimensional map data cube through the spectral reconstruction algorithm. Since the detector needs to collect spatial geometric information and spectral information at the same time during the imaging process, so as to construct the geometric image and spectral image of the target area, it is impossible to take into account the spatial resolution and spectral resolution of the collected pixels at the same time. Push-broom hyperspectral imaging technology uses a push-broom spectral imaging system to collect push-broom data of the target area. The push-broom spectral imaging system consists of a front objective lens, a single slit assembly and a planar detector. During the push-broom process, only one band of hyperspectral data is obtained at each moment. Finally, the complete hyperspectral data of the target area is obtained by splicing the hyperspectral data obtained at each moment. Compared with snapshot hyperspectral imaging technology, this imaging method can simultaneously obtain images with high spectral resolution and high spatial resolution. Therefore, the application of push-broom spectral imaging systems is becoming more and more extensive.

[0004] At present, push-broom hyperspectral imaging systems have been widely used in the field of aerospace remote sensing. Due to the high cost of commonly used aerospace hyperspectral imaging systems and the fact that aerospace platforms are at a certain height from the ground, it is impossible to detect small-scale details in near-ground areas. Therefore, new hyperspectral imaging systems based on light and small UAV platforms are increasingly widely used. By mounting the push-broom spectral imaging system on a UAV, the flight path and flight speed of the UAV are controlled to achieve push-broom imaging of the target area. However, the UAV is easily affected by factors such as airflow and vibration, and it is difficult to maintain the same speed and posture during the push-broom process, resulting in sampling dislocation and missing problems during the imaging process, affecting the integrity and accuracy of the hyperspectral image. In addition, with the improvement of ground resolution, it is necessary to simultaneously increase the sampling frame rate during push-broom imaging or reduce the flight speed of the UAV in order to obtain a hyperspectral image with high spatial resolution. However, the increase in the sampling frame rate (i.e., the shortening of the exposure time) will lead to a decrease in the light flux of the push-broom spectral imaging system, thereby reducing the signal-to-noise ratio of the imaging system, resulting in a decrease in the clarity and contrast of the hyperspectral image, while the reduction in flight speed will affect the imaging efficiency.

[0005] In summary, the existing push-broom spectral imaging system based on UAV has problems such as sampling misalignment, sampling missing, and the inability to balance imaging efficiency and imaging quality. Summary of the invention

[0006] To this end, the technical problem to be solved by the present invention is to overcome the problems of sampling misalignment, sampling missing and the inability to balance imaging efficiency and imaging quality in the push-broom spectral imaging system based on drones in the prior art.

[0007] In order to solve the above technical problems, the present invention provides a multi-slit hyperspectral imaging system based on an unmanned aerial vehicle, comprising:

[0008] A front objective lens, used for imaging the input light path of the imaging area;

[0009] A multi-slit assembly, comprising a plurality of slits, for sampling the output image plane of the front objective lens using the plurality of slits to obtain a plurality of sampling strips corresponding to the number of slits;

[0010] The spectroscopic imaging component specifically comprises:

[0011] A collimating lens group, used for collimating a plurality of sampling strips output by the multi-slit assembly;

[0012] A dispersion prism assembly, comprising a first Amisi prism and a second Amisi prism symmetrically arranged,

[0013] The oblique incident surface of the first Amisi prism serves as the incident surface of the dispersion prism group, and is used to perform dispersion spectroscopy on the multiple sampling strips output by the collimator group; the oblique incident surface of the second Amisi prism serves as the exit surface of the dispersion prism group, and is used to perform dispersion spectroscopy on the multiple sampling strips output by the first Amisi prism, and eliminate the aberration in the multiple sampling strips output by the first Amisi prism, and output multiple sampling strips after dispersion spectroscopy;

[0014] A focusing lens group, used for focusing and imaging the multiple sampling strips after dispersion and splitting output by the dispersion prism group, respectively, to obtain multiple spectral data corresponding to the multiple sampling strips after dispersion and splitting;

[0015] A detector, used to receive the plurality of spectral data, collect spatial information of the area to be imaged, and fuse the plurality of spectral data with the spatial information to obtain a plurality of spectral images;

[0016] The host computer is connected to the detector for fusing the multiple spectral images to obtain a target spectral image of the area to be imaged.

[0017] Preferably, the number of slits in the multi-slit assembly is 3 to 10; and / or

[0018] The width of each slit is 8 μm to 25 μm; and / or

[0019] The distance between two adjacent slits is 1 mm to 1.5 mm.

[0020] Preferably, the distance between the first Amisi prism and the second Amisi prism is 10 to 20 mm;

[0021] The first Amisi prism and the second Amisi prism are both obtained by gluing a right-angle prism and a roof prism.

[0022] Preferably, the vertex angles of the right-angle prism and the roof prism are both -15° to 15°;

[0023] The center thickness of the right-angle prism is 8 to 15 mm;

[0024] The central thickness of the roof prism is 5 to 10 mm.

[0025] Preferably, the materials of the right-angle prism and the roof prism include fused quartz and calcium fluoride.

[0026] Preferably, the front objective lens is a projection double Gauss objective lens with a focal length of 22.9 mm;

[0027] The number of slits in the multi-slit assembly is 10, the width of each slit is 20 μm, and the spacing between two adjacent slits is 1 mm;

[0028] The length of the spectroscopic imaging component along the propagation direction of the optical path is 208 mm, and the aperture number of the spectroscopic imaging component is 4.5;

[0029] The first Amisi prism and the second Amisi prism are both obtained by gluing a right-angle prism made of H-LAF54 and a roof prism made of H-ZF88GT;

[0030] The vertex angle of the right-angle prism is 13.5°, and the center thickness of the right-angle prism is 8 mm; the vertex angle of the roof prism is 5.8°, and the center thickness of the roof prism is 5 mm;

[0031] The focal lengths of the collimating lens group and the focusing lens group are both 65 mm, and the object space is both 0.11.

[0032] Preferably, the host computer includes:

[0033] A feature point recognition module is used to calculate the difference between the grayscale value of each pixel point in each spectral image and the grayscale value of its adjacent pixel points; and take the pixel points whose grayscale value differences with the adjacent pixel points are greater than or less than a first preset threshold as the feature points of the spectral image;

[0034] A feature point region construction module is used to obtain a set of pixels in the spectral image whose distance from the feature point is less than a preset distance; every two adjacent pixels in the pixel point set are taken as a set of pixel point pairs to obtain a pixel point pair set of the feature point;

[0035] A feature point descriptor acquisition module is used to obtain the binary feature value of each pixel pair based on the grayscale value of the first pixel and the second pixel in each pixel pair; based on the binary feature values ​​of all pixel pairs in the pixel pair set of the feature point, obtain the binary feature value of the feature point;

[0036] The feature point matching module is used to calculate the similarity of each feature point based on the binary feature value of the feature point in each spectral image, so as to superimpose the multiple spectral images to obtain a target spectral image.

[0037] Preferably, the host computer further includes:

[0038] A sampling missing pixel point identification module is used to take the pixel points with a gray value of 0 in the target spectral image as sampling missing pixel points;

[0039] A target pixel point acquisition module is used to take the pixel point with the same coordinate as the sampling missing pixel point in each spectral image as the target pixel point;

[0040] The missing pixel compensation module is used to calculate the grayscale average value of the target pixel whose grayscale value is not 0, and use the grayscale average value as the grayscale value of the sampled missing pixel.

[0041] The present invention also provides a multi-slit hyperspectral imaging method based on a drone, which is applied to the multi-slit hyperspectral imaging system based on a drone, comprising:

[0042] Using the front objective lens to image the input light path of the imaging area;

[0043] The output image plane of the front objective lens is sampled by using a plurality of slits in the multi-slit assembly to obtain a plurality of sampling strips corresponding to the number of slits;

[0044] A plurality of sampling strips outputted by the multi-slit assembly are collimated by a collimator group; a plurality of sampling strips outputted by the collimator group are dispersed and split by a first Amisi prism; a plurality of sampling strips outputted by the first Amisi prism are dispersed and split by a second Amisi prism group, and aberrations in the plurality of sampling strips outputted by the first Amisi prism are eliminated to output a plurality of sampling strips after dispersion and split; a plurality of sampling strips after dispersion and split are respectively focused and imaged by a focusing lens group to obtain a plurality of spectral data corresponding to the plurality of sampling strips after dispersion and split;

[0045] Using a detector to receive the plurality of spectral data, collecting spatial information of the area to be imaged, and fusing the plurality of spectral data with the spatial information to obtain a plurality of spectral images;

[0046] The multiple spectral images are fused to obtain a target spectral image of the area to be imaged.

[0047] Preferably, fusing the multiple spectral images to obtain a target spectral image of the area to be imaged comprises:

[0048] Calculate the difference between the grayscale value of each pixel in each spectral image and the grayscale value of its adjacent pixel; take the pixel whose grayscale value difference with the adjacent pixel is greater than or less than a first preset threshold as the feature point of the spectral image;

[0049] Acquire a set of pixels in the spectral image whose distance from the feature point is less than a preset distance; take every two adjacent pixels in the set of pixels as a set of pixel pairs to obtain a set of pixel pairs of the feature point;

[0050] Obtaining a binary eigenvalue of the pixel pair based on the grayscale value of the first pixel and the second pixel in each group of pixel pairs; obtaining a binary eigenvalue of the feature point based on the binary eigenvalues ​​of all pixel pairs in the pixel pair set of the feature point;

[0051] Based on the binary feature values ​​of the feature points in each spectral image, the similarity of each feature point is calculated, so as to superimpose the multiple spectral images to obtain a target spectral image;

[0052] The pixel points with gray value of 0 in the target spectral image are taken as sampling missing pixel points;

[0053] The pixel point in each spectral image that has the same coordinates as the sampling missing pixel point is taken as the target pixel point;

[0054] The grayscale average value of the target pixel points whose grayscale values ​​are not 0 is calculated, and the grayscale average value is used as the grayscale value of the sampling missing pixel point.

[0055] The multi-slit hyperspectral imaging system based on a drone provided in the present application utilizes multiple slits on a multi-slit assembly to sample different positions in a target area, and multiple sampling strips can be obtained by shooting one frame, which not only improves the sampling frame rate and sampling efficiency, but also increases the light flux in the sampling process, thereby improving the signal-to-noise ratio of the imaging system; however, since multiple sampling strips collected by the multi-slit assembly may be aliased, the spectral resolution is reduced, resulting in the spectral images generated based on these strips having poor color reproduction and low contrast, which in turn causes the spectral images to be unable to clearly reflect the characteristics of the target area. Therefore, the present application designs a dispersion prism assembly, which The invention comprises a first Amisi prism and a second Amisi prism. Since the strips collected by different slits have different refractive indices in the prism material, when the strips collected by different slits are incident from the oblique incident surface of the first Amisi prism, according to the law of refraction, these strips will propagate in different directions to achieve beam dispersion. At the same time, since the symmetrically arranged Amisi prisms have symmetrical optical paths and dispersion characteristics, the second Amisi prism can further enhance the dispersion effect of the first Amisi prism, and at the same time offset the aberration existing in the strips emitted by the first Amisi prism, fully separate the strips collected by different slits, prevent the strips collected by multiple slits from aliasing and distortion, and improve the spectral resolution. The present application uses multiple slits to sample the target area at the same time to obtain multiple sampling strips, which not only avoids the sampling missing and misalignment problems caused by the jitter of the drone, but also improves the light flux and sampling efficiency of the imaging system. The strips collected by multiple slits are dispersed and split by a dispersion prism group, thereby improving the spectral resolution, and taking into account the imaging quality while improving the imaging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

[0057] Figure 1 Optical path diagram of the multi-slit hyperspectral imaging system based on drone provided for this application;

[0058] Figure 2 Schematic diagram of the structure of the multi-slit hyperspectral imaging system based on drone provided for this application;

[0059] Figure 3 A schematic diagram of a multi-slit assembly provided for this application;

[0060] Figure 4 A schematic diagram of a spectrum image output by the detector provided in this application;

[0061] Figure 5 A flowchart of the working principle of the multi-slit hyperspectral imaging system based on a drone provided for this application;

[0062] Figure 6 A schematic diagram of a target pixel image output by each slit in the multi-slit hyperspectral imaging system based on a drone provided in Example 1;

[0063] Figure 7 A hyperspectral image output by the multi-slit hyperspectral imaging system based on a drone provided in Example 1;

[0064] Explanation of the reference numerals in the specification: 1. front objective lens; 2. multi-slit assembly; 3. spectroscopic imaging assembly; 31. collimating lens group; 32. dispersion prism group; 321. first Amisi prism; 322. second Amisi prism; a. right-angle prism; b. roof prism; 33. focusing lens group; 4. detector; 5. host computer. DETAILED DESCRIPTION

[0065] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0066] See also Figure 1 and Figure 2 , Figure 1 The figure shows the optical path diagram of the multi-slit hyperspectral imaging system based on a drone provided by the present application. Figure 2 The figure shows a schematic diagram of a multi-slit hyperspectral imaging system based on a UAV provided in the present application. The multi-slit hyperspectral imaging system based on a UAV includes a front objective lens 1, a multi-slit assembly 2, a spectroscopic assembly 3, a detector 4, and a host computer 5 connected to the detector 4, which are arranged sequentially along the propagation direction of the optical path.

[0067] The front objective lens 1 is used to image the input light path of the area to be imaged.

[0068] The multi-slit assembly 2 includes a plurality of slits, and is used to sample the output image plane of the front objective lens 1 using the plurality of slits to obtain a plurality of sampling strips corresponding to the number of the sampling slits.

[0069] The spectroscopic imaging assembly 3 comprises a collimating lens group 31 , a dispersing prism group 32 and a focusing lens group 33 which are sequentially and discretely arranged along the propagation direction of the optical path.

[0070] The collimating lens group 31 is used to collimate the multiple sampling strips output by the multi-slit assembly.

[0071] The dispersion prism group 32 includes a first Amisci prism 321 and a second Amisci prism 322 which are symmetrically arranged.

[0072] For example, the first Amis prism 321 and the second Amis prism 322 are bilaterally symmetrical with respect to a direction perpendicular to the optical axis.

[0073] The oblique incident surface of the first Amisi prism 321 serves as the incident surface of the dispersion prism group 32 , and is used to perform dispersion and spectral separation on the multiple sampling strips output by the collimator group 31 .

[0074] The oblique incident surface of the second Amisi prism 322 serves as the exit surface of the dispersion prism group 32, which is used to disperse and split the multiple sampling strips output by the first Amisi prism 321, eliminate the aberrations in the multiple sampling strips output by the first Amisi prism 321, and output multiple sampling strips after dispersion and splitting.

[0075] The focusing lens group 33 is used to focus and image the multiple sampling strips after dispersion and splitting output by the dispersion prism group 32, so as to obtain multiple spectral data corresponding to the multiple sampling strips after dispersion and splitting.

[0076] The detector 4 is used to receive a plurality of spectral data, collect spatial information of the area to be imaged, and fuse the plurality of spectral data with the spatial information to obtain a plurality of spectral images.

[0077] In a specific example of the present application, the detector 4 is a two-dimensional surface array detector.

[0078] The host computer 5 is used to fuse multiple spectral images to obtain a target spectral image.

[0079] The multi-slit hyperspectral imaging system based on a drone provided in the present application utilizes multiple slits on a multi-slit assembly to sample different positions in a target area, and multiple sampling strips can be obtained by shooting one frame, which not only improves the sampling frame rate and sampling efficiency, but also increases the light flux in the sampling process, thereby improving the signal-to-noise ratio of the imaging system; however, since multiple sampling strips collected by the multi-slit assembly may be aliased, the spectral resolution is reduced, resulting in the spectral images generated based on these strips having poor color reproduction and low contrast, which in turn causes the spectral images to be unable to clearly reflect the characteristics of the target area. Therefore, the present application designs a dispersion prism assembly, which The invention comprises a first Amisi prism and a second Amisi prism. Since the strips collected by different slits have different refractive indices in the prism material, when the strips collected by different slits are incident from the oblique incident surface of the first Amisi prism, according to the law of refraction, these strips will propagate in different directions to achieve beam dispersion. At the same time, since the symmetrically arranged Amisi prisms have symmetrical optical paths and dispersion characteristics, the second Amisi prism can further enhance the dispersion effect of the first Amisi prism, and at the same time offset the aberration existing in the strips emitted by the first Amisi prism, fully separate the strips collected by different slits, prevent the strips collected by multiple slits from aliasing and distortion, and improve the spectral resolution. The present application uses multiple slits to sample the target area at the same time to obtain multiple sampling strips, which not only avoids the sampling missing and misalignment problems caused by the jitter of the drone, but also improves the light flux and sampling efficiency of the imaging system. The strips collected by multiple slits are dispersed and split by a dispersion prism group, thereby improving the spectral resolution, and taking into account the imaging quality while improving the imaging efficiency.

[0080] Furthermore, the number of slits of the multi-slit assembly 2 is 3 to 10, and the number of slits may be 3, 5, 7 or 10.

[0081] Specifically, the number of slits is related to the sampling missing probability. The more slits there are, the smaller the sampling missing probability is, and the higher the accuracy of the hyperspectral image obtained is. For example, if the sampling missing probability of a single slit component is P, then the sampling missing probability of a multi-slit component with k slits under the same environment is P. k , the signal-to-noise ratio is improved by about However, increasing the number of slits will inevitably increase the volume of the imaging system, which cannot meet the requirement of lightweight instrument. Therefore, the present application limits the number of slits, thereby reducing the probability of sampling miss, improving sampling efficiency and taking into account the volume of the imaging system.

[0082] Furthermore, the width of each slit is 8 μm to 25 μm, and the slit width may be 8 μm, 10 μm, 15 μm, 20 μm or 25 μm.

[0083] Specifically, the slit width affects the spectral resolution and spatial resolution. According to the principle of spectroscopy, the slit width determines the wavelength range of the light it samples. The narrower the slit, the smaller the wavelength range that can pass through, the narrower the bandwidth of the spectrum, and the higher the spectral resolution. Moreover, when the slit width is too large, light from different positions enters the slit at the same time and mixes together, reducing the ability to resolve the spatial details of the object, thereby reducing the spatial resolution. However, when the target area to be imaged is too large, the slit width is too narrow, which affects the sampling efficiency. Therefore, the present application limits the slit width, thereby improving the spectral resolution and spatial resolution while taking into account the imaging efficiency.

[0084] Furthermore, the spacing between two adjacent slits is 1 mm to 1.5 mm, and the spacing between adjacent slits may be 1 mm, 1.3 mm or 1.5 mm.

[0085] Specifically, the imaging system is often carried on a drone for use. The interval between the slits determines whether the same ground pixel can be accurately and repeatedly sampled during the push-scan process of the drone, and further determines the stitching accuracy of multiple slit sampling strips. Therefore, the present application can improve the sampling accuracy of each slit by limiting the spacing between the slits, ensure that there is no overlapping part between the multiple sampling strips received by the planar detector, thereby improving the stitching accuracy, so that the hyperspectral image of the target area obtained after stitching can more accurately reflect the characteristic information of the target area.

[0086] Furthermore, when using the multi-slit assembly 2 for sampling, in order to ensure that the strips obtained by sampling each slit will not be aliased, the multiple sampled strips need to be fully separated. The dispersion prism group 3 designed in the present application utilizes the dispersion characteristics of the Amici prism to prevent the strips collected by multiple slits from being aliased and distorted, thereby improving the spectral resolution.

[0087] Specifically, the distance between the first Amisi prism 321 and the second Amisi prism 322 is 10-20 mm, and the distance may be 10 mm, 12 mm, 14 mm, 16 mm, 18 mm or 20 mm.

[0088] When the distance between the first Amisi prism 321 and the second Amisi prism 322 increases, the propagation optical path of the light beam between the first Amisi prism 321 and the second Amisi prism 322 increases, so that the optical path difference between light beams of different wavelengths changes, which in turn affects the distribution and spacing of the interference fringes. The larger the distance, the narrower the interference fringes, the denser the fringes, and the higher the spectral resolution after splitting, so that different wavelengths of light can be distinguished more finely. However, if the distance is too large, the volume of the imaging system will increase, and if the system structure is not compact, it will be more likely to cause displacement and deformation of optical elements, resulting in unstable splitting effect. Therefore, the present application limits the distance between the first Amisi prism 321 and the second Amisi prism 322, thereby reducing the system volume while ensuring the splitting effect, thereby improving the integration and optical stability of the imaging system.

[0089] Furthermore, the first Amis prism 321 and the second Amis prism 322 are both obtained by gluing a right-angle prism a and a roof prism b.

[0090] Optionally, materials of the right-angle prism a and the roof prism b include fused quartz and calcium fluoride.

[0091] Furthermore, the vertex angles of the right-angle prism a and the roof prism b are both -15° to 15°, and the vertex angles may be -15°, -10°, -5°, 0°, 5°, 10° or 15°.

[0092] Furthermore, the center thickness of the right-angle prism a is 8 to 15 mm, and the center thickness of the right-angle prism a may be 8 mm, 12 mm or 15 mm.

[0093] Furthermore, the center thickness of the roof prism b is 5-10 mm, and the center thickness of the roof prism b may be 5 mm, 7 mm or 10 mm.

[0094] For example, Figure 3 FIG. 1 is a schematic diagram of a multi-slit assembly provided in an embodiment of the present application. The multi-slit assembly uses 10 slits to perform strip-type discrete sampling on the image plane of the front objective lens 1, and a certain gap is left between the sampling strips. After the sampling strips are dispersed and split by the spectroscopic imaging assembly designed in the present application, multiple spectral data are output and recorded by a planar detector, and multiple spectral images are output, such as Figure 4 The figure shows the spectral image output by the planar detector. It can be seen from the figure that multiple spectral images are independent of each other and have no aliasing. Finally, by fusing multiple spectral images, a hyperspectral image of the target area can be obtained.

[0095] Furthermore, the host computer 5 specifically includes:

[0096] A feature point recognition module is used to calculate the difference between the grayscale value of each pixel point in each spectral image and the grayscale value of its adjacent pixel points; and take the pixel points whose grayscale value differences with the adjacent pixel points are greater than or less than a first preset threshold as the feature points of the spectral image;

[0097] The feature point region construction module is used to obtain a set of pixels in the spectral image whose distance from the feature point is less than a preset distance; every two adjacent pixels in the pixel point set are taken as a set of pixel point pairs to obtain a pixel point pair set of the feature point;

[0098] A feature point descriptor acquisition module is used to obtain the binary feature value of each pixel pair based on the grayscale value of the first pixel and the second pixel in each pixel pair; based on the binary feature values ​​of all pixel pairs in the pixel pair set of the feature point, obtain the binary feature value of the feature point;

[0099] For example, if the grayscale value of the first pixel in a pixel pair is greater than the grayscale value of the second pixel, the binary eigenvalue of the pixel pair is 1; if the grayscale value of the first pixel is less than or equal to the grayscale value of the second pixel, the binary eigenvalue of the pixel pair is 0; the binary eigenvalue of the feature point is obtained by combining the binary eigenvalues ​​of all pixel pairs in the corresponding pixel pair set.

[0100] The feature point matching module is used to calculate the similarity of each feature point based on the binary feature value of the feature point in each spectral image, so as to superimpose multiple spectral images to obtain a target spectral image.

[0101] Feature point matching is performed based on the similarity of feature points, so that some pixels may be missing in the target spectral image obtained by superimposing multiple spectral images, that is, the sampling missing problem. In order to improve the integrity and accuracy of the spectral image and more accurately reflect the characteristics of the target area, it is also necessary to identify and supplement the missing pixels in the target spectral image.

[0102] Based on this, in some embodiments of the present application, the host computer further includes:

[0103] A sampling missing pixel point recognition module is used to take the pixel points with a gray value of 0 in the target spectral image as sampling missing pixel points;

[0104] A target pixel point acquisition module is used to take the pixel point with the same coordinates as the sampling missing pixel point in each spectral image as the target pixel point;

[0105] The missing pixel compensation module is used to calculate the grayscale average value of the target pixel whose grayscale value is not 0, and use the grayscale average value as the grayscale value of the sampled missing pixel.

[0106] like Figure 5 The flowchart of the working principle of the multi-slit hyperspectral imaging system based on the drone provided by the present application is shown. In the process of shooting a frame, each slit in the multi-slit assembly can sample the target area and obtain a swath of the target area. As the drone pushes and sweeps, each slit can obtain multiple swaths of the target area. Therefore, after the push sweep is completed, the multiple swaths obtained by sampling each slit are first spliced ​​to obtain the complete swath corresponding to the slit. Finally, the complete swaths corresponding to all slits are image fused to obtain a hyperspectral image of the target area. Since multiple slits will sample the same position in the target area during the drone's push sweep of the ground, repeated sampling of the target area is achieved, reducing the probability of missing samples.

[0107] Specifically, the steps of splicing multiple swaths sampled from a slit include: obtaining the position and attitude information of the UAV during the push-scan process from the airborne global positioning system and the inertial navigation unit (IMU), calculating the transformation matrix between the swaths according to the position and attitude information corresponding to each strip, realizing the alignment between the multiple swaths by calculating the transformation matrix, and finally completing the splicing of the swaths to obtain the complete swath corresponding to each slit.

[0108] Based on the multi-slit hyperspectral imaging system based on a drone provided in the above embodiment, the embodiment of the present application further provides a multi-slit hyperspectral imaging method based on a drone, which specifically includes:

[0109] Using the front objective lens to image the input light path of the imaging area;

[0110] The output image plane of the front objective lens is sampled by using a plurality of slits in the multi-slit assembly to obtain a plurality of sampling strips corresponding to the number of slits;

[0111] A collimator group is used to collimate the multiple sampling strips output by the multi-slit assembly; a first Amisi prism is used to perform dispersion spectroscopy on the multiple sampling strips output by the collimator group; a second Amisi prism group is used to perform dispersion spectroscopy on the multiple sampling strips output by the first Amisi prism, and aberrations in the multiple sampling strips output by the first Amisi prism are eliminated to output multiple sampling strips after dispersion spectroscopy; a focusing lens group is used to focus and image the multiple sampling strips after dispersion spectroscopy to obtain multiple spectral data corresponding to the multiple sampling strips after dispersion spectroscopy;

[0112] The detector is used to receive multiple spectral data, collect spatial information of the area to be imaged, and fuse the multiple spectral data with the spatial information to obtain multiple spectral images;

[0113] Multiple spectral images are fused to obtain the target spectral image of the area to be imaged.

[0114] Specifically, in some embodiments of the present application, fusing multiple spectral images to obtain a target spectral image of the area to be imaged includes:

[0115] Calculate the difference between the grayscale value of each pixel in each spectral image and the grayscale value of its adjacent pixel; take the pixel whose grayscale value difference with the adjacent pixel is greater than or less than a first preset threshold as the feature point of the spectral image;

[0116] Obtain a set of pixels in the spectral image whose distance from the feature point is less than a preset distance; take every two adjacent pixels in the pixel set as a set of pixel pairs to obtain a pixel pair set of the feature point;

[0117] Obtaining a binary eigenvalue of the pixel pair based on the grayscale value of the first pixel and the second pixel in each group of pixel pairs; obtaining a binary eigenvalue of the feature point based on the binary eigenvalues ​​of all pixel pairs in the pixel pair set of the feature point;

[0118] Based on the binary eigenvalues ​​of the feature points in each spectral image, the similarity of each feature point is calculated, so as to superimpose multiple spectral images to obtain a target spectral image;

[0119] The pixels with gray value of 0 in the target spectral image are regarded as the sampling missing pixels;

[0120] The pixel points in each spectral image that have the same coordinates as the sampled missing pixel points are taken as target pixel points;

[0121] Calculate the grayscale average of the target pixels whose grayscale values ​​are not 0, and use the grayscale average as the grayscale value of the sampling missing pixels.

[0122] The strip misalignment and missing problems obtained during sampling in the existing single-slit hyperspectral imaging system will cause serious distortion of the stitched image. Several methods for correcting image distortion have been proposed in the prior art, such as: 1. An optical system with a common objective lens for hyperspectral imaging and array imaging is proposed, and low-precision position and attitude data obtained by consumer-grade positioning and inertial navigation devices and auxiliary array cameras are used to perform geometric correction on hyperspectral images; 2. Independent array cameras are used to provide high-precision attitude data to assist in the geometric correction of hyperspectral images; 3. Scale-invariant feature transformation and random sampling consensus algorithm are used to perform geometric correction on images, and additional RGB images are used to assist in obtaining high-precision correction results. These methods can correct the distortion caused by sampling misalignment. For the missing sampling part, the existing methods use interpolation algorithms to fill the missing values, but the interpolation algorithm is difficult to restore the spectral data of the missing sampling area with high fidelity, especially when the drone is disturbed for a long time or the attitude changes greatly, the large-scale sampling missing cannot be restored, resulting in low accuracy and integrity of the hyperspectral image.

[0123] The multi-slit hyperspectral imaging method based on a drone provided in the embodiment of the present application obtains spectral information of multiple strips of the area to be imaged through one frame of shooting, realizes repeated sampling of the target area, and reduces the probability of sampling missing. Moreover, when fusing the multiple images obtained by sampling, firstly, feature points of each image are identified, and then the multiple images are superimposed to identify the sampling missing area. For the area without missing, the target grayscale value of the area can be obtained by calculating the grayscale average value of all images in the area. For the area with missing, the grayscale value of the missing area is filled by obtaining the grayscale average value of the non-missing image in the area. A new image fusion algorithm is designed while reducing the probability of sampling missing, so that a hyperspectral image with high integrity and accuracy can be obtained.

[0124] The technical solution of the present application is described in more detail below in conjunction with a plurality of embodiments, but it should be understood that the following embodiments are only for explaining and illustrating the technical solution and do not limit the scope of the present application.

[0125] Example 1

[0126] The present embodiment provides a multi-slit hyperspectral imaging system based on a drone, and the multi-slit hyperspectral imaging system based on a drone includes: a front objective lens, a multi-slit component, a spectroscopic component, a detector, and a host computer connected to the detector.

[0127] The front objective lens is a projection double Gauss objective lens with a focal length of 22.9mm.

[0128] The number of slits in the multi-slit assembly is 10, the width of each slit is 20 μm, and the interval between two adjacent slits is 1 mm.

[0129] The length of the spectroscopic imaging component along the propagation direction of the light path is 208 mm; the aperture number of the spectroscopic imaging component is 4.5.

[0130] The spectroscopic imaging component includes a collimator lens group, a dispersion prism group and a focusing lens group; the focal lengths of the collimator lens group and the focusing lens group are both 65 mm, and the object space is both 0.11.

[0131] The dispersion prism group includes a first Amisi prism and a second Amisi prism that are symmetrically arranged; the interval between the first Amisi prism and the second Amisi prism is 10 mm.

[0132] The first Amisi prism and the second Amisi prism are both obtained by gluing a right-angle prism made of H-LAF54 and a roof prism made of H-ZF88GT.

[0133] The vertex angle of the right-angle prism is 13.5°, and the center thickness of the right-angle prism is 8mm. The vertex angle of the roof prism is 5.8°, and the center thickness of the roof prism is 5mm; the light path turns 29° after passing through the first Amisi prism and the second Amisi prism.

[0134] Table 1 shows the relevant parameters of the multi-slit component in the multi-slit hyperspectral imaging system based on the drone provided in this embodiment:

[0135] Table 1

[0136] Slit length / mm Slit width / μm Slit spacing / mm Number of slits / root Field of view / ° 10 20 1 10 47.17

[0137] Table 2 shows the strip parameters of the multi-slit assembly shown in Table 1 projected onto the ground:

[0138] Table 2

[0139] Strip width / m Ground sampling interval / m 8.7 0.017

[0140] Table 3 shows the flight parameters for push-broom photography of a UAV equipped with a multi-slit hyperspectral imaging system:

[0141] Table 3

[0142] <![CDATA[Flight speed / m·s -1 > Flight altitude / m 2 20

[0143] Under the above parameters, the traditional single-slit imaging system is affected by the jitter of the UAV, causing the UAV posture state to change during the push-scan process. The sampling missing probability of the spectral image obtained by the push-scan is 10%, and the image acquisition frame rate is 118pbs.

[0144] In the multi-slit hyperspectral imaging system based on a drone provided in this embodiment, 588 sampling strips are obtained after each slit is pushed and scanned at a distance of 10m. According to the actual posture data during push scanning, a projection mapping algorithm is used to splice and obtain a continuous image corresponding to each slit, such as Figure 6 As shown in the figure, (1) to (10) are the continuous images corresponding to each slit, and there are missing pixels in each image. By superimposing the continuous images corresponding to all slits and compensating the sampling missing pixels in the target spectrum image obtained after fusion, we can get the following: Figure 7 Hyperspectral image shown.

[0145] By comparing the hyperspectral image with the hyperspectral image obtained by the traditional single-slit imaging system, it is found that the signal-to-noise ratio of the hyperspectral image obtained in this embodiment is 3.2 times higher than that of the hyperspectral image obtained by the traditional single-slit imaging system, and the probability of missing pixels is reduced from 10% to (10%)10.

[0147] Example 2

[0148] The present embodiment provides a multi-slit hyperspectral imaging system based on a drone, and the multi-slit hyperspectral imaging system based on a drone includes: a front objective lens, a multi-slit component, a spectroscopic component, a detector, and a host computer connected to the detector.

[0149] The front objective lens is a projection double Gauss objective lens with a focal length of 22.9mm.

[0150] The number of slits in the multi-slit assembly is 3, the width of each slit is 10 μm, and the interval between two adjacent slits is 1.5 mm.

[0151] The length of the spectroscopic imaging component along the propagation direction of the light path is 208 mm; the aperture number of the spectroscopic imaging component is 4.5.

[0152] The spectroscopic imaging component includes a collimator lens group, a dispersion prism group and a focusing lens group; the focal lengths of the collimator lens group and the focusing lens group are both 65 mm, and the object space is both 0.11.

[0153] The dispersion prism group includes a first Amisi prism and a second Amisi prism; the interval between the first Amisi prism and the second Amisi prism is 15 mm.

[0154] The first Amisi prism and the second Amisi prism are both obtained by gluing a right-angle prism made of H-LAF54 and a roof prism made of H-ZF88GT.

[0155] The vertex angle of the right-angle prism is 0°, and the center thickness of the right-angle prism is 12.5 mm. The vertex angle of the roof prism is 10°, and the center thickness of the roof prism is 7.5 mm.

[0156] Table 4 shows the relevant parameters of the multi-slit component in the multi-slit hyperspectral imaging system based on the drone provided in this embodiment:

[0157] Table 4

[0158] Slit length / mm Slit width / μm Slit spacing / mm Number of slits / root Field of view / ° 8 10 1.5 3 38.51

[0159] Table 5 shows the strip parameters of the multi-slit assembly shown in Table 4 projected onto the ground:

[0160] Table 5

[0161] Strip width / m Ground sampling interval / m 8.7 0.017

[0162] Table 6 shows the flight parameters for push-broom photography of a UAV equipped with a multi-slit hyperspectral imaging system:

[0163] Table 6

[0164] <![CDATA[Flight speed / m·s -1 > Flight altitude / m 1.25 30

[0165] Under the above parameters, the traditional single-slit imaging system is affected by the jitter of the UAV, causing the UAV posture state to change during the push-scan process. The sampling missing probability of the spectral image obtained by the push-scan is 9%, and the image acquisition frame rate is 96pbs.

[0166] In the multi-slit hyperspectral imaging system based on a drone provided in this embodiment, 385 sampling strips are obtained for each slit after push-scanning at a distance of 5m. The continuous images corresponding to each slit are spliced ​​using a projection mapping algorithm based on the implemented posture data during push-scanning. The continuous images corresponding to all the slits are superimposed and the sampling missing pixels in the target spectral image obtained after fusion are compensated to obtain a hyperspectral image.

[0167] By comparing the hyperspectral image with the hyperspectral image obtained by the traditional single-slit imaging system, it is found that the signal-to-noise ratio of the hyperspectral image obtained in this embodiment is 1.7 times higher than that of the hyperspectral image obtained by the traditional single-slit imaging system, and the probability of missing pixels is reduced from 9% to (9%)10.

[0169] Example 3

[0170] The present embodiment provides a multi-slit hyperspectral imaging system based on a drone, and the multi-slit hyperspectral imaging system based on a drone includes: a front objective lens, a multi-slit component, a spectroscopic component, a detector, and a host computer connected to the detector.

[0171] The front objective lens is a projection double Gauss objective lens with a focal length of 22.9mm.

[0172] The number of slits in the multi-slit assembly is 6, the width of each slit is 15 μm, and the interval between two adjacent slits is 2 mm.

[0173] The length of the spectroscopic imaging component along the propagation direction of the light path is 208 mm; the aperture number of the spectroscopic imaging component is 4.5.

[0174] The spectroscopic imaging component includes a collimator lens group, a dispersion prism group and a focusing lens group; the focal lengths of the collimator lens group and the focusing lens group are both 65 mm, and the object space is both 0.11.

[0175] The dispersion prism group includes a first Amisi prism and a second Amisi prism; the interval between the first Amisi prism and the second Amisi prism is 20 mm.

[0176] The first Amisi prism and the second Amisi prism are both obtained by gluing a right-angle prism made of H-LAF54 and a roof prism made of H-ZF88GT.

[0177] The vertex angle of the right-angle prism is -13.8°, and the center thickness of the right-angle prism is 15mm. The vertex angle of the roof prism is -10°, and the center thickness of the roof prism is 10mm.

[0178] Table 7 shows the relevant parameters of the multi-slit component in the multi-slit hyperspectral imaging system based on the drone provided in this embodiment:

[0179] Table 7

[0180] Slit length / mm Slit width / μm Slit spacing / mm Number of slits / root Field of view / ° 11 15 2 6 51.31

[0181] Table 8 shows the strip parameters of the multi-slit assembly shown in Table 7 projected onto the ground:

[0182] Table 8

[0183] Strip width / m Ground sampling interval / m 16.8 0.023

[0184] Table 9 shows the flight parameters for push-broom photography of a UAV equipped with a multi-slit hyperspectral imaging system:

[0185] Table 9

[0186] <![CDATA[Flight speed / m·s -1 > Flight altitude / m 1.5 35

[0187] Under the above parameters, the traditional single-slit imaging system is affected by the jitter of the UAV, causing the UAV posture state to change during the push-scan process. The sampling missing probability of the spectral image obtained by the push-scan is 15%, and the image acquisition frame rate is 65pbs.

[0188] In the multi-slit hyperspectral imaging system based on the drone provided in this embodiment, 217 sampling strips are obtained for each slit after push-scanning at a distance of 5m. The continuous images corresponding to each slit are spliced ​​using a projection mapping algorithm based on the implemented posture data during push-scanning. The continuous images corresponding to all the slits are superimposed and the sampling missing pixels in the target spectral image obtained after fusion are compensated to obtain a hyperspectral image.

[0189] By comparing the hyperspectral image with the hyperspectral image obtained by the traditional single-slit imaging system, it is found that the signal-to-noise ratio of the hyperspectral image obtained in this embodiment is 2.45 times higher than that of the hyperspectral image obtained by the traditional single-slit imaging system, and the probability of missing pixels is reduced from 15% to (15%)10.

[0191] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0192] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0195] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A multi-slit hyperspectral imaging system based on an unmanned aerial vehicle, characterized in that: include: A front objective lens, used for imaging the input light path of the imaging area; A multi-slit assembly, comprising a plurality of slits, for sampling the output image plane of the front objective lens using the plurality of slits to obtain a plurality of sampling strips corresponding to the number of slits; The spectroscopic imaging component specifically comprises: A collimating lens group, used for collimating a plurality of sampling strips output by the multi-slit assembly; A dispersion prism group, comprising a first Amisi prism and a second Amisi prism symmetrically arranged, wherein the oblique incident surface of the first Amisi prism serves as the incident surface of the dispersion prism group, and is used to perform dispersion spectroscopy on a plurality of sampling strips output by the collimator group; the oblique incident surface of the second Amisi prism serves as the exit surface of the dispersion prism group, and is used to perform dispersion spectroscopy on a plurality of sampling strips output by the first Amisi prism, and eliminate aberrations in the plurality of sampling strips output by the first Amisi prism, and output a plurality of sampling strips after dispersion spectroscopy; A focusing lens group, used for focusing and imaging the multiple sampling strips after dispersion and splitting output by the dispersion prism group, respectively, to obtain multiple spectral data corresponding to the multiple sampling strips after dispersion and splitting; A detector, used to receive the plurality of spectral data, collect spatial information of the area to be imaged, and fuse the plurality of spectral data with the spatial information to obtain a plurality of spectral images; The host computer is connected to the detector for fusing the multiple spectral images to obtain a target spectral image of the area to be imaged.

2. The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle according to claim 1, characterized in that: The number of slits in the multi-slit assembly is 3 to 10; and / or The width of each slit is 8 μm to 25 μm; and / or The distance between two adjacent slits is 1 mm to 1.5 mm.

3. The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle according to claim 1, characterized in that: The distance between the first Amisi prism and the second Amisi prism is 10 to 20 mm; The first Amisi prism and the second Amisi prism are both obtained by gluing a right-angle prism and a roof prism.

4. The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle according to claim 3, characterized in that: The vertex angles of the right-angle prism and the roof prism are both -15° to 15°; The center thickness of the right-angle prism is 8 to 15 mm; The central thickness of the roof prism is 5 to 10 mm.

5. The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle according to claim 3, characterized in that: The materials of the right-angle prism and the roof prism include fused quartz and calcium fluoride.

6. The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle according to claim 1, characterized in that: The front objective lens is a projection double Gauss objective lens with a focal length of 22.9 mm; The number of slits in the multi-slit assembly is 10, the width of each slit is 20 μm, and the spacing between two adjacent slits is 1 mm; The length of the spectroscopic imaging component along the propagation direction of the optical path is 208 mm, and the aperture number of the spectroscopic imaging component is 4.5; The first Amisi prism and the second Amisi prism are both obtained by gluing a right-angle prism made of H-LAF54 and a roof prism made of H-ZF88GT; The vertex angle of the right-angle prism is 13.5°, and the center thickness of the right-angle prism is 8 mm; the vertex angle of the roof prism is 5.8°, and the center thickness of the roof prism is 5 mm; The focal lengths of the collimating lens group and the focusing lens group are both 65 mm, and the object space is both 0.

11.

7. The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle according to claim 1, characterized in that: The host computer comprises: A feature point recognition module is used to calculate the difference between the grayscale value of each pixel point in each spectral image and the grayscale value of its adjacent pixel points; and take the pixel points whose grayscale value differences with the adjacent pixel points are greater than or less than a first preset threshold as the feature points of the spectral image; A feature point region construction module is used to obtain a set of pixels in the spectral image whose distance from the feature point is less than a preset distance; every two adjacent pixels in the pixel point set are taken as a set of pixel point pairs to obtain a pixel point pair set of the feature point; A feature point descriptor acquisition module is used to obtain the binary feature value of each pixel pair based on the grayscale value of the first pixel and the second pixel in each pixel pair; based on the binary feature values ​​of all pixel pairs in the pixel pair set of the feature point, obtain the binary feature value of the feature point; The feature point matching module is used to calculate the similarity of each feature point based on the binary feature value of the feature point in each spectral image, so as to superimpose the multiple spectral images to obtain a target spectral image.

8. The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle according to claim 7, characterized in that: The host computer also includes: A sampling missing pixel point identification module is used to take the pixel points with a gray value of 0 in the target spectral image as sampling missing pixel points; A target pixel point acquisition module is used to take the pixel point with the same coordinate as the sampling missing pixel point in each spectral image as the target pixel point; The missing pixel compensation module is used to calculate the grayscale average value of the target pixel whose grayscale value is not 0, and use the grayscale average value as the grayscale value of the sampled missing pixel.

9. A multi-slit hyperspectral imaging method based on an unmanned aerial vehicle, characterized in that: The multi-slit hyperspectral imaging system based on an unmanned aerial vehicle as described in any one of claims 1 to 8 comprises: Using the front objective lens to image the input light path of the imaging area; The output image plane of the front objective lens is sampled by using a plurality of slits in the multi-slit assembly to obtain a plurality of sampling strips corresponding to the number of slits; A plurality of sampling strips outputted by the multi-slit assembly are collimated by a collimator group; a plurality of sampling strips outputted by the collimator group are dispersed and split by a first Amisi prism; a plurality of sampling strips outputted by the first Amisi prism are dispersed and split by a second Amisi prism group, and aberrations in the plurality of sampling strips outputted by the first Amisi prism are eliminated to output a plurality of sampling strips after dispersion and split; a plurality of sampling strips after dispersion and split are respectively focused and imaged by a focusing lens group to obtain a plurality of spectral data corresponding to the plurality of sampling strips after dispersion and split; Using a detector to receive the plurality of spectral data, collecting spatial information of the area to be imaged, and fusing the plurality of spectral data with the spatial information to obtain a plurality of spectral images; The multiple spectral images are fused to obtain a target spectral image of the area to be imaged.

10. The multi-slit hyperspectral imaging method based on an unmanned aerial vehicle according to claim 9, characterized in that: The multiple spectral images are fused to obtain a target spectral image of the area to be imaged, including: Calculate the difference between the grayscale value of each pixel in each spectral image and the grayscale value of its adjacent pixel; take the pixel whose grayscale value difference with the adjacent pixel is greater than or less than a first preset threshold as the feature point of the spectral image; Acquire a set of pixels in the spectral image whose distance from the feature point is less than a preset distance; take every two adjacent pixels in the set of pixels as a set of pixel pairs to obtain a set of pixel pairs of the feature point; Obtaining a binary eigenvalue of the pixel pair based on the grayscale value of the first pixel and the second pixel in each group of pixel pairs; obtaining a binary eigenvalue of the feature point based on the binary eigenvalues ​​of all pixel pairs in the pixel pair set of the feature point; Based on the binary feature values ​​of the feature points in each spectral image, the similarity of each feature point is calculated, so as to superimpose the multiple spectral images to obtain a target spectral image; The pixel points with gray value of 0 in the target spectral image are taken as sampling missing pixel points; The pixel point in each spectral image that has the same coordinates as the sampling missing pixel point is taken as the target pixel point; The grayscale average value of the target pixel points whose grayscale values ​​are not 0 is calculated, and the grayscale average value is used as the grayscale value of the sampling missing pixel point.

Images