UAV hyperspectral imaging method, system and equipment

Abstract

The present invention provides a method, system and device for unmanned aerial vehicle hyperspectral imaging, the method comprising: taking the single imaging range of a hyperspectral camera as a unit grid, dividing the expected imaging range into grids; taking the central grid as a reference, obtaining the rotation angle of each grid corresponding to the hyperspectral camera, the rotation angle including the roll angle rotation angle and the pitch angle rotation angle; sequentially obtaining the rotation angle corresponding to each grid in the expected imaging range to obtain a rotation angle set; after the hyperspectral camera is located at a specified position, controlling the roll angle rotation angle and the pitch angle rotation angle of the hyperspectral camera according to the rotation angle set to obtain a corresponding regional image set; performing splicing processing on the regional images in the regional image set to obtain an actual imaging image. The present invention can realize large-angle hyperspectral scanning imaging of a single fixed point, optimize the imaging quality, and improve the overall imaging speed.

Description

Technical Field

[0001] The present invention relates to the technical field of hyperspectral imaging, and in particular to a method, system and device for unmanned aerial vehicle (UAV) hyperspectral imaging. Background Art

[0002] A drone refers to an unmanned aircraft that is controlled by a radio remote control device and a self-contained program control device; a hyperspectral camera refers to a camera made using hyperspectral imaging technology, which needs to be mounted on a gimbal and pushed and scanned (i.e., the camera is moved at a constant speed and stably) to complete the imaging of the entire hyperspectral spectrum.

[0003] There are two existing hyperspectral push-broom imaging solutions:

[0004] 1. When collecting hyperspectral data, the push-scan process is achieved by flying the drone at a constant speed. The gimbal stabilizes the camera angle and thus stabilizes the imaging.

[0005] 2. The hyperspectral camera itself has the function of completing push-broom imaging within a certain spatial range in a static state. The specific imaging method is: when the drone is hovering, the hyperspectral camera itself completes a push-broom imaging process; after the drone moves to the next position and hovers again, the hyperspectral camera completes another push-broom imaging process. Repeat this process, and stitch all the push-broom imaging images together to achieve the overall push-broom effect.

[0006] The above two existing hyperspectral push-broom imaging solutions have the following shortcomings:

[0007] 1. The UAV may experience jitter, attitude changes, and heading deviation during flight, which will cause changes in the imaging conditions such as the angle, position, or height of the hyperspectral camera, thereby affecting the imaging quality.

[0008] 2. The hyperspectral camera needs to add a corresponding translation push-scan structure to realize the movement of the hyperspectral camera itself or related components, and then complete the push-scan imaging process. The imaging effect is better than the first solution, but the field of view of each imaging is smaller and the overall imaging speed is slow. Summary of the invention

[0009] The present invention aims to solve at least one of the technical problems in the above-mentioned technology to a certain extent. To this end, one purpose of the present invention is to propose a method for unmanned aerial vehicle hyperspectral imaging, which can realize large-angle hyperspectral scanning imaging of a single fixed point, optimize the imaging quality, and improve the overall imaging speed.

[0010] The second object of the present invention is to provide a UAV hyperspectral imaging device that can achieve large-angle hyperspectral scanning imaging of a single fixed point, optimize the imaging quality, and improve the overall imaging speed.

[0011] The third object of the present invention is to provide a UAV hyperspectral imaging system that can achieve large-angle hyperspectral scanning imaging of a single fixed point, optimize the imaging quality, and improve the overall imaging speed.

[0012] To achieve the above object, a first embodiment of the present invention provides a method for unmanned aerial vehicle hyperspectral imaging, comprising:

[0013] The expected imaging range is grid-divided using the single imaging range of the hyperspectral camera as a unit grid;

[0014] Taking the central grid as a reference, obtaining the rotation angle of the hyperspectral camera corresponding to each grid, wherein the rotation angle includes a roll angle rotation angle and a pitch angle rotation angle;

[0015] Sequentially obtain the rotation angle corresponding to each grid in the expected imaging range to obtain a rotation angle set;

[0016] After the hyperspectral camera is located at a specified position, the roll angle rotation angle and the pitch angle rotation angle of the hyperspectral camera are controlled according to the rotation angle set to obtain a corresponding regional image set;

[0017] The regional images in the regional image set are stitched together to obtain an actual imaging image.

[0018] According to a method for hyperspectral imaging of unmanned aerial vehicles of an embodiment of the present invention, a grid-based expected imaging range is obtained by grid expansion based on a single imaging range, and the rotation angle of the hyperspectral camera corresponding to each grid is determined based on the central grid, and a rotation angle set arranged in grid order is obtained; during actual imaging, it is only necessary to control the roll angle and pitch angle of the camera to change according to the rotation angle set at a fixed point position to obtain a regional image set of each grid, and the actual imaging image can be obtained by splicing them in sequence. It can be understood that the above-mentioned embodiment of the present invention can simultaneously expand the width and length of the overall imaging at a fixed point position by controlling the rotation of the two dimensions of the roll angle rotation angle and the pitch angle rotation angle; thereby, the hyperspectral camera can achieve large-angle hyperspectral scanning imaging at a single fixed point, while optimizing the imaging quality, and improving the overall imaging speed.

[0019] In addition, the UAV hyperspectral imaging method proposed in the above embodiment of the present invention may also have the following additional technical features:

[0020] Optionally, it also includes:

[0021] Use row and column numbering to number each grid.

[0022] Optionally, sequentially acquiring the rotation angle corresponding to each grid in the expected imaging range to acquire a set of rotation angles includes:

[0023] Traverse each row or column, arrange the rotation angles corresponding to the traversed grids in order, and obtain a rotation angle set.

[0024] Optionally, it also includes:

[0025] Associate each rotation angle in the rotation angle set with the number of the corresponding grid.

[0026] Optionally, the step of obtaining the corresponding regional image set further includes:

[0027] According to the association, a set of regional images associated with the numbers of each grid is obtained.

[0028] Optionally, the performing stitching processing on the regional images in the regional image set to obtain the actual imaging image includes:

[0029] According to the numbers of the grids corresponding to the regional images in the regional image set, the regional images are spliced ​​to obtain actual imaging images.

[0030] Optionally, sequentially acquiring the rotation angle corresponding to each grid in the expected imaging range to obtain a rotation angle set includes:

[0031] Traverse each row or column, arrange the roll angle rotation angle and the pitch angle rotation angle corresponding to the traversed grid in order, and obtain an ordered set of roll angle rotation angles and an ordered set of pitch angle rotation angles;

[0032] Sequentially combine each roll angle rotation angle in the ordered set of roll angle rotation angles with each pitch angle rotation angle in the ordered set of pitch angle rotation angles, or sequentially combine each pitch angle rotation angle in the ordered set of pitch angle rotation angles with each roll angle rotation angle in the ordered set of roll angle rotation angles to obtain a rotation angle set.

[0033] Optionally, it also includes:

[0034] Preset the acquisition parameters of the hyperspectral camera, including rotation mode, flight altitude, and rotation angle or rotation area.

[0035] To achieve the above-mentioned object, a second embodiment of the present invention proposes a UAV hyperspectral imaging device, including an MCU, an IMU, a hyperspectral camera, a motor MX and a motor MY; the IMU, the hyperspectral camera, the motor MX and the motor MY are respectively connected to the MCU;

[0036] The UAV hyperspectral imaging device also includes a program; after being executed by the MCU, the program can implement the above-mentioned UAV hyperspectral imaging method.

[0037] According to the unmanned aerial vehicle hyperspectral imaging device of the above embodiment of the present invention, it only needs to add a motor MX for realizing the pitch angle change and a motor MY for realizing the roll angle change, so as to achieve the expansion of the width and length of the overall imaging at a single fixed point at the same time, which can not only optimize the imaging quality but also improve the overall imaging speed.

[0038] To achieve the above objectives, a third aspect of the present invention provides a UAV hyperspectral imaging system, including a UAV and the UAV hyperspectral imaging device as described above, which is arranged on the UAV.

[0039] According to the UAV hyperspectral imaging system of the above embodiment of the present invention, the UAV hyperspectral imaging device carried by the UAV can realize large-angle hyperspectral scanning imaging of a single fixed point, which can not only optimize the imaging quality but also improve the overall imaging speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 (a) Schematic diagram of the relationship between a hyperspectral camera and its single imaging area;

[0041] Figure 1 (b) is a schematic diagram showing the relationship between the long side, height and imaging angle in a single imaging area of ​​a hyperspectral camera;

[0042] Figure 1 (c) is a schematic diagram showing the relationship between the short side, height and imaging angle in a single imaging area of ​​a hyperspectral camera;

[0043] Figure 2 Schematic diagram of the relationship between the rotation angle of the hyperspectral camera and the acquisition area;

[0044] Figure 3 A schematic diagram of a process flow of a UAV hyperspectral imaging method provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of grid division and grid numbering of an expected imaging range in a method for unmanned aerial vehicle hyperspectral imaging provided by an embodiment of the present invention;

[0046] Figure 5 (a) to (c) are schematic diagrams of single imaging during the fixed-point rotation push-scanning process of an embodiment of the present invention;

[0047] Figure 6 A schematic diagram of the structure of a UAV hyperspectral imaging device provided by an embodiment of the present invention;

[0048] Figure 7 A schematic diagram of the coordinates of a structural model of a UAV hyperspectral imaging device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0050] The present invention uses a single imaging range as a unit grid expansion to obtain a gridded expected imaging range, and uses the central grid as a reference to determine the rotation angle of the hyperspectral camera corresponding to each grid, and obtains a rotation angle set arranged in grid order; during actual imaging, it is only necessary to control the roll angle and pitch angle of the camera at a fixed point according to the rotation angle set to obtain a regional image set of each grid, thereby realizing rapid acquisition of actual imaging images with optimized imaging quality.

[0051] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0053] See also Figure 1 (a)-(c) and Figure 2 . Figure 1 (a) Schematic diagram of the relationship between a hyperspectral camera and its single imaging area; Figure 1 (b) is a schematic diagram showing the relationship between the long side, height and imaging angle in a single imaging area of ​​a hyperspectral camera; Figure 1 (c) is a schematic diagram showing the relationship between the short side, height and imaging angle in a single imaging area of ​​a hyperspectral camera; Figure 2 Schematic diagram of the relationship between the rotation angle of the hyperspectral camera and the collection area.

[0054] Figure 1 h in (a) to (c) represents the height between the hyperspectral camera and the ground; Figure 1 (b) shows the relationship between the long side d1 of the single imaging area and the imaging angle α1 at a height of h; Figure 1 (c) shows the relationship between the short side d2 of the single imaging area and the imaging angle α2 at a height of h.

[0055] Since the rotation direction of the hyperspectral camera is parallel to the sides of the camera's single imaging area, that is, the pitch angle rotation direction is parallel to the short side, and the roll angle rotation direction is parallel to the long side. The hyperspectral camera completes the scanning action by rotating, so the collection area after rotation is related to the rotation angle.

[0056] According to Figure 1 (b) and Figure 1 (c) It can be seen that the principle of solving the relationship between the rotation angles of the pitch angle and the roll angle and the acquisition area is consistent.

[0057] like Figure 2 As shown in the figure, point O is the acquisition point of the hyperspectral camera, h is the height of the hyperspectral camera relative to the ground, α is the imaging angle of the hyperspectral camera, β is the rotation angle, d is the acquisition area of ​​the field of view of the hyperspectral camera at the height h, and e is the sampling area after the hyperspectral camera is rotated by β at the height h. It can be seen that the relationship between the rotation angle and the acquisition area of ​​the hyperspectral camera is:

[0058]

[0059] Use θ i represents the rotation angle each time, and x represents the acquisition area each time, then:

[0060]

[0061] When the flight altitude of the UAV is determined, α, h and d are all constants, then each θ i There is an x i Corresponding to it.

[0062] In actual use, the hyperspectral camera can have two scanning modes:

[0063] 1. Keep the rotation angle consistent each time, then θ1=θ2=…=θ n-1 =θ, formula (2) can be converted to:

[0064]

[0065] 2. Keep the area consistent each time, then x1=x2=…=x n-1 =x, formula (2) can be converted to:

[0066]

[0067] Figure 3 A schematic diagram of the process of a UAV hyperspectral imaging method provided by an embodiment of the present invention.

[0068] like Figure 3 As shown, an embodiment of the present invention provides a method for unmanned aerial vehicle hyperspectral imaging, which at least includes the following steps S1 to S5.

[0069] In step S1, the expected imaging range is grid-divided using the single imaging range of the hyperspectral camera as a unit grid.

[0070] In this embodiment, the expected imaging range can be understood as the size of the imaging area / range of the ideal actual imaging image, that is, the frame size of the actual imaging image.

[0071] like Figure 4 As shown, it is a schematic diagram of the expected imaging range after grid division. It can be understood that the expected imaging range of this embodiment is an expansion based on the single imaging range of the hyperspectral camera. For example, the expected imaging range can be a single imaging range of 3*3, 5*7... Of course, the actual range size can also be comprehensively considered by factors such as the fixed point height during imaging and the camera field of view.

[0072] In some specific implementations, after step S1, further comprising:

[0073] S11: Number each grid using row and column numbering method.

[0074] The row and column numbering method refers to numbering the grids in each row and column in ordinal or alphabetical order. A combination of letters and numbers can be used for numbering, such as A1, A2...; B1, B2...

[0075] As a specific example, the grid numbers of the expected imaging range after grid division are shown as follows: Figure 4 shown. Figure 4 What is shown is that the numbering is performed by column, with letters representing columns and ordinal numbers representing rows. It can be understood that the numbering can also be performed by row, with letters representing rows and ordinal numbers representing columns.

[0076] Here, by numbering the grids, it can serve as a grid identification to facilitate subsequent operations.

[0077] In step S2, the rotation angle of the hyperspectral camera corresponding to each grid is obtained based on the central grid of the expected imaging range after grid division, and the rotation angle includes a roll angle rotation angle and a pitch angle rotation angle.

[0078] The central grid described in this embodiment refers to a grid that is as close as possible to the center point of the expected imaging range after grid division, and does not necessarily have to be the clear most central grid.

[0079] In some specific implementations, three optional methods for selecting the center grid are as follows:

[0080] 1. If the number of rows and columns of the expected imaging range after grid division is an odd number, then the clear center grid is selected as the center grid of this embodiment. For example, for a 3*3 grid, the second grid in the second row is directly selected as the center grid.

[0081] 2. If the number of rows or columns of the expected imaging range after grid division is an even number, a grid is selected from the grids close to the clear center point position as the center grid of this embodiment according to the preset algorithm. For example, for a 3*4 grid, the second grid in the second row or the third grid in the second row is selected as the center grid.

[0082] 3. According to a preset algorithm, a grid is selected from the expected imaging range after grid division as the central grid of this embodiment. For example, the grid in the middle of the first row / middle row / closest to the middle point is directly selected as the central grid of this embodiment.

[0083] The rotation angle described in this embodiment includes a roll angle rotation angle and a pitch angle rotation angle. The roll angle rotation angle refers to the angle between the horizontal axis of the hyperspectral camera and the horizontal line, which represents the side tilt angle of the hyperspectral camera. For example, the imaging angle of the hyperspectral camera can be controlled to tilt left or right through the roll angle; the pitch angle rotation angle refers to the angle between the X-axis of the hyperspectral camera and the horizontal plane, which represents the tilt angle of the hyperspectral camera on the horizontal plane. For example, the imaging angle of the hyperspectral camera can be controlled to adjust upward or downward through the pitch angle.

[0084] In this embodiment, the rotation angle of the grid refers to the rotation values ​​of the current roll angle and pitch angle of the grid when the hyperspectral camera collects the grid image, taking the central grid as a reference.

[0085] In this embodiment, according to the above formula (1), formula (3) and formula (4), the rotation angle of the hyperspectral camera corresponding to each grid based on the central grid can be calculated. Preferably, the above calculation process, that is, the process of calculating and obtaining the rotation angle of each grid according to the above formula (1), formula (3) and formula (4), is implemented in the form of a hyperspectral camera running a corresponding computer algorithm program, which will be more efficient and accurate.

[0086] In step S3, the rotation angle corresponding to each grid in the expected imaging range is obtained in sequence to obtain a rotation angle set.

[0087] The term "in sequence" refers to following a certain order.

[0088] In some specific implementations, the step S3 is specifically:

[0089] Traverse each row or column, arrange the rotation angles corresponding to the traversed grids in the traversal order, and obtain a rotation angle set.

[0090] In other words, you can traverse by row or by column to get the rotation angle of each grid one by one. Figure 4 For the expected imaging range of the grid numbers shown, the order of “traversal by rows” is “A1, A2, A3…An, B1, B2, B3…Bn, X1, X2, X3…Xn”; and the order of “traversal by columns” is “A1, B1…X1, A2, B2…X2, A3, B3…X3…”.

[0091] It is preferred to use a traversal method corresponding to the grid numbering method. That is, traversal is performed in an order corresponding to the above-mentioned row and column numbering method. For example, Figure 4 The expected imaging range of the grid number shown is obtained by "traversing by column" to obtain the rotation angle corresponding to each grid under each column, that is, traversing in sequence to obtain A1 (roll angle, pitch angle), A2 (roll angle, pitch angle)...

[0092] The rotation angle set is an ordered set, which refers to a set of rotation angles corresponding to each grid arranged in the order of acquisition. In this embodiment, the rotation angle set can be represented in two ways:

[0093] One is to use a set to represent the rotation angles corresponding to each grid. That is, the roll angle rotation angle and the pitch angle rotation angle are combined. For example, the rotation angle set {A1(pitch angle α F1 , roll angle α H1 ), A2(pitch angle α F2 , roll angle α H2 )…}.

[0094] Another method is to use two sets to represent the rotation angles corresponding to each grid. That is, two sets are used to represent the roll angle rotation angle and the pitch angle rotation angle respectively.

[0095] Corresponding to the above-mentioned “another” way of expressing the rotation angle set, in a specific example, the specific implementation process includes:

[0096] S31: traverse each row or each column, and arrange the roll rotation angles and pitch rotation angles corresponding to the traversed grids in order, to obtain an ordered set of roll rotation angles and an ordered set of pitch rotation angles;

[0097] S32: Combining the roll angle rotation angles in the ordered set of roll angle rotation angles with the pitch angle rotation angles in the ordered set of pitch angle rotation angles in sequence, or combining the pitch angle rotation angles in the ordered set of pitch angle rotation angles with the roll angle rotation angles in sequence to obtain a rotation angle set.

[0098] In some specific implementations, each rotation angle in the rotation angle set is associated with a number of its corresponding grid.

[0099] Here, by establishing an association relationship between each rotation angle and its corresponding grid number, it can facilitate subsequent data processing and provide good identification when data is consulted. It can be understood that the regional image set corresponding to the sequence of the rotation angle set will also have a corresponding relationship between each regional image in the regional image set and the grid number through the above association relationship. That is, the grid number corresponding to each regional image in the regional image set can be obtained through this association relationship. Based on this, it can not only serve as a basis for subsequent "stitching processing"; but also facilitate confirmation / query of its related processing data when there is a question about any regional image in the actual imaging image.

[0100] In step S4, after the hyperspectral camera is located at the designated position, the roll angle rotation angle and the pitch angle rotation angle of the hyperspectral camera are controlled according to the rotation angle set to obtain a corresponding regional image set.

[0101] The designated position refers to a hovering position designated by a preset, that is, a position for collecting images. In this embodiment, the hyperspectral camera collects images only at the designated position throughout the entire process, and the position does not change, but the angle changes during the collection process.

[0102] In this embodiment, the specific acquisition process of the hyperspectral camera will start from the first group of rotation angles according to the rotation angle set (such as Figure 5 As shown in a, assuming that the first group corresponds to the A1 grid), after the hyperspectral camera is rotated accordingly, a corresponding frame of regional image is acquired; then, the rotation angle of the second group (such as Figure 5 b, the second group corresponds to the A2 grid), after the hyperspectral camera is rotated accordingly, a corresponding frame of regional image is acquired; and so on, until the last group of rotation angles (such as Figure 5 c, the last group corresponds to the C3 grid), and a frame of regional images corresponding to each of the grids is acquired. These regional images are arranged in the order of acquisition and imaging, and the regional image set is obtained. For example, the regional image set {A1, A2, A3…}.

[0103] In some specific implementations, the step S4, after that, further includes:

[0104] S41: According to the association, a set of regional images associated with the numbers of each grid is obtained.

[0105] The association refers to the association between each rotation angle in the rotation angle set described in step S3 and the number of the corresponding grid.

[0106] In this embodiment, the regional image set also belongs to an ordered set, and its order corresponds to the order of the rotation angle set; from the above association, it can be seen that there is a corresponding relationship between each rotation angle in the rotation angle set and the number of each grid. Based on this, the corresponding relationship between each frame image and the number of each grid in the regional image set can be obtained.

[0107] In step S5, the regional images in the regional image set are spliced ​​to obtain an actual imaging image.

[0108] It can be understood that, through the above step S4, it is possible to obtain multiple frames of regional images of the actual regional range corresponding to the expected imaging range of the hyperspectral camera when the hyperspectral camera is located at a specified position in a real scene.

[0109] In some specific implementations, based on the correspondence between the above-mentioned regional image set and each grid number, the actual imaging image can be obtained by splicing each frame of regional image in the regional image set.

[0110] For example, compare the region image set {A1, A2, A3...} Figure 4 By splicing the grid number schematic diagram, the complete actual imaging image can be obtained.

[0111] In some other specific implementations, the actual image can also be obtained by splicing the image set {A1, A2, A3...} directly according to the "acquisition order" of the rotation angles of each grid in step S2, that is, the traversal order, and in combination with the "number of rows and columns" confirmed when performing grid division in step S1. For example, the regional image set {A1, A2, A3...} is compared with the original "column traversal" method, and the "expected imaging range" has a total of 3 rows and 4 columns, and splicing starts from the first column, splicing A1, A2, A3 and A4 into the first column, splicing A5 to A8 into the second column, and so on.

[0112] The drone hyperspectral imaging method of this embodiment adopts a fixed-point rotation push-broom method to achieve high-spectral scanning imaging at a large angle. Compared with the prior art, it can not only overcome the problem of poor imaging quality caused by acquisition during flight, but also solve the problem of small imaging field of view and slow overall imaging speed of the traditional translation push-broom method. Therefore, the drone hyperspectral imaging method of this embodiment can have the advantages of optimizing imaging quality and improving overall imaging speed.

[0113] See also Figure 5 (a) to (c), Figure 5(a) to (c) are schematic diagrams of single imaging during the fixed-point rotation push-scanning process according to an embodiment of the present invention.

[0114] The present invention is further developed based on the above-mentioned embodiment, and also provides a method for high-spectral imaging of a drone using a rotary sweeper.

[0115] In this embodiment, the expected imaging range of the hyperspectral camera is as follows: Figure 5 (a) shows the grid division and grid area numbering. That is, the single acquisition area of ​​the hyperspectral camera is numbered in sequence using letters + numbers.

[0116] Setting in hyperspectral camera Figure 5 The grid area B2 in (a) is the acquisition area directly below the hyperspectral image, that is, the rotation angle of the grid area B2 is 0. At the same time, the acquisition parameters of the hyperspectral camera are preset: rotation mode (same angle or same area), rotation angle / rotation area (same angle / same area), flight height, and length and width rotation times.

[0117] The parameter "Rotation Mode" indicates that the camera rotates at the same angle or the same acquisition area each time it rotates. If you choose to rotate at the same angle, you need to set the parameter "Rotation Angle", that is, set the angle value required for the camera to rotate each time; if you choose to rotate the same area, you need to set the parameter "Go to Area", that is, set the acquisition area range added each time the camera rotates. "Flight Altitude" refers to the flight altitude of the drone when collecting hyperspectral images. It can be set by the user or obtained by the device from the drone.

[0118] The parameters “long rotation times” and “wide rotation times” correspond to the number of rotations required for the long side of the field of view to complete the total acquisition area of ​​the hyperspectral camera (i.e. Figure 4 The X in Xn minus 1) and the number of turns required for the short side (i.e. Figure 4 The n in Xn minus 1).

[0119] When the parameter "Go to Mode" is set to "Same Angle", the parameters "Rotation Angle", "Flight Altitude", "Long Rotation Times" and "Wide Rotation Times" are substituted into formula (3) to calculate the acquisition area for each time; when the parameter "Go to Mode" is set to "Same Area", the parameters "Rotation Area", "Flight Altitude", "Long Rotation Times" and "Wide Rotation Times" are substituted into formula (4) to calculate the rotation angle for each time.

[0120] The image acquisition process of the hyperspectral camera is:

[0121] 1. Determine the rotation angle of each grid area and obtain the rotation angle set.

[0122] like Figure 5 The expected imaging range shown in (a)-(c) is formed by rotating the pitch angle and roll angle of the hyperspectral camera three times respectively. Based on equations (1), (3) and (4) of the above embodiment, it can be deduced that Figure 5 The set of rotation angles corresponding to the expected imaging area of ​​(a)-(c) is:

[0123]

[0124] 2. Before collecting data after hovering, first control the roll angle of the hyperspectral camera to α H1 , the pitch angle is rotated to α F1 ; The imaging angle after rotation is as follows Figure 5 As shown in (a), a frame of image is captured, corresponding to the capture area A1.

[0125] 3. After collecting area A1, control the pitch angle of the hyperspectral camera to rotate to α F2 , the imaging angle after rotation is as follows Figure 5 As shown in (b), a frame of image is collected, corresponding to the collection area A2; then, the pitch angle of the hyperspectral camera is controlled to rotate to α F3 , collect the image of area A3.

[0126] 4. Control the roll angle of the hyperspectral camera to α H2 , and control the pitch angle back to α F1 , collect the image of area B1; then, control the roll angle of the hyperspectral camera to be fixed at α H2 , and then rotate the pitch angle to α F2 and α F3 , and respectively acquire corresponding images, namely, acquisition area B2 and acquisition area B3.

[0127] 5. Control the roll angle of the hyperspectral camera to switch to the next angle in the roll angle rotation angle set, i.e. α H3 , and fix the roll angle; then control the pitch angle to rotate to α F1 , α F2 and α F3 , respectively collect the corresponding collection area C1, collection area C2 and collection area C3.

[0128] 6. When the roll angle and pitch angle are fully rotated, the imaging angle reaches Figure 5 (c) shows that the collection is completed.

[0129] 7. Compare the images of each acquisition area Figure 5 The grid area numbers in (a) are stitched together to obtain a complete actual imaging image.

[0130] See also Figure 6 and Figure 7 , Figure 6 A schematic diagram of the structure of a UAV hyperspectral imaging device provided by an embodiment of the present invention; Figure 7 A schematic diagram of the coordinates of a structural model of a UAV hyperspectral imaging device provided in an embodiment of the present invention.

[0131] The embodiment of the present invention is further expanded based on any of the above embodiments, and also provides a drone hyperspectral imaging device.

[0132] like Figure 6 As shown, the drone hyperspectral imaging device of this embodiment includes an MCU, an IMU, a hyperspectral camera, a motor MX, and a motor MY; the IMU, the hyperspectral camera, the motor MX, and the motor MY are connected to the MCU respectively. The IMU refers to an inertial measurement unit, which can measure the three-axis attitude angle (or angular velocity) and acceleration of the hyperspectral camera.

[0133] The UAV hyperspectral imaging device further includes a program; after being executed by the MCU, the program can implement the steps included in the UAV hyperspectral imaging method described in any of the above embodiments. The specific steps are not repeated here, please refer to the records of the above embodiments for details.

[0134] In some specific embodiments, the drone hyperspectral imaging device is Figure 7 As shown, it also includes a power interface; the power interface is connected to the MCU and is used to power the UAV hyperspectral imaging device.

[0135] like Figure 7 As shown, O is the origin of coordinate axes X and Y, C is the hyperspectral camera in the UAV hyperspectral imaging device of this embodiment, MX is a motor for controlling the pitch angle, MY is a motor for controlling the roll angle, and R is a fixing bracket of the hyperspectral camera.

[0136] The mechanical control content of the UAV hyperspectral imaging device of this embodiment during its hyperspectral acquisition and imaging process includes:

[0137] For the motor MX, the MCU controls the motor MX to rotate to a specified angle and reads the angle of the IMU to confirm whether it has rotated to the specified angle. When the MCU confirms that the motor MX has rotated to the specified angle, it triggers the hyperspectral camera to capture data.

[0138] For motor MY, MCU controls motor MY to rotate to the specified angle and reads the angle of IMU to confirm whether it has rotated to the correct position. When MCU confirms that motor MY has rotated to the correct position, it triggers the hyperspectral camera to capture data.

[0139] Based on the above control, the collection of images of each area is completed.

[0140] Next, combine Figure 5 Explain the motor control acquisition process of the hyperspectral camera:

[0141] Assumptions Figure 5 is the area to be collected, where A1 represents the data collected by the hyperspectral camera once, A2 represents the data collected after the motor MX rotates once, and A3 represents the data collected after the motor MX rotates twice. By analogy, An represents the data collected after the motor MX rotates n-1 times. It can be seen that A1 to An represent all the data collected by the motor MY at the same angle by rotating the motor MX; similarly, B1 to Bn represent all the data collected by the motor MY rotating once and then not moving, and then rotating the motor MX.

[0142] It can be understood that the UAV hyperspectral imaging device of this embodiment achieves the extension of imaging length by changing the pitch angle through the motor MX; and expands the imaging width by changing the roll angle through the motor MY.

[0143] A drone hyperspectral imaging device provided in this embodiment controls the rotation of the hyperspectral camera in two dimensions, namely, the roll angle and the pitch angle. Based on the correspondence between a preset single rotation angle and a scanning area, and in combination with precise imaging at each rotation position, the drone hyperspectral imaging device can achieve large-angle scanning imaging at a fixed position, and the overall imaging speed is improved compared to the traditional translational push-scanning method.

[0144] Based on the above embodiment, the embodiment of the present invention further provides a UAV hyperspectral imaging system, including a UAV and a UAV hyperspectral imaging device as described in the above embodiment arranged on the UAV. Here, the specific structure of the UAV hyperspectral imaging device, its working principle and the UAV hyperspectral imaging method are not described in detail. For details, please refer to the description of the above embodiment.

[0145] The embodiment provides a UAV hyperspectral imaging system, in which the hyperspectral camera can realize large-angle hyperspectral scanning imaging when the UAV is in a hovering state, which not only optimizes the imaging quality but also improves the overall imaging speed.

[0146] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0148] 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.

[0149] 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.

[0150] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0151] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0152] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0153] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0154] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0155] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0156] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0157] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. UAV hyperspectral imaging method, characterized in that: include: The expected imaging range is divided into grids using the single imaging range of the hyperspectral camera as the unit grid; Taking the central grid as a reference, obtain the rotation angle of the hyperspectral camera corresponding to each grid, wherein the rotation angle includes the roll angle and the pitch angle; Sequentially obtain the rotation angle corresponding to each grid in the expected imaging range to obtain a rotation angle set; After the hyperspectral camera is located at a specified position, the roll angle and the pitch angle of the hyperspectral camera are controlled according to the rotation angle set to obtain a corresponding regional image set; The regional images in the regional image set are stitched together to obtain an actual imaging image.

2. The UAV hyperspectral imaging method according to claim 1, wherein: Also includes: Use row and column numbering to number each grid.

3. The UAV hyperspectral imaging method according to claim 2, wherein: The step of sequentially acquiring the rotation angle corresponding to each grid in the expected imaging range to acquire a set of rotation angles includes: Traverse each row or column, arrange the rotation angles corresponding to the traversed grids in order, and obtain a rotation angle set.

4. The UAV hyperspectral imaging method according to claim 3, wherein: Also includes: Associate each rotation angle in the rotation angle set with the number of its corresponding grid.

5. The UAV hyperspectral imaging method according to claim 4, wherein: After obtaining the corresponding regional image set, the method further includes: According to the association, a set of regional images associated with the numbers of the respective grids is obtained.

6. The UAV hyperspectral imaging method according to claim 5, wherein: The step of performing splicing processing on the regional images in the regional image set to obtain an actual imaging image includes: The regional images in the regional image set are spliced ​​according to the grid numbers corresponding to the regional images to obtain actual imaging images.

7. The UAV hyperspectral imaging method according to claim 1, wherein: The step of sequentially acquiring the rotation angle corresponding to each grid in the expected imaging range to obtain a rotation angle set includes: Traverse each row or column, arrange the roll rotation angles and pitch rotation angles corresponding to the traversed grids in order, and obtain an ordered set of roll rotation angles and an ordered set of pitch rotation angles; Sequentially combine each roll angle rotation angle in the ordered set of roll angle rotation angles with each pitch angle rotation angle in the ordered set of pitch angle rotation angles, or sequentially combine each pitch angle rotation angle in the ordered set of pitch angle rotation angles with each roll angle rotation angle in the ordered set of roll angle rotation angles to obtain a rotation angle set.

8. The UAV hyperspectral imaging method according to claim 1, wherein: Also includes: Preset the acquisition parameters of the hyperspectral camera, including rotation mode, flight altitude, and rotation angle or rotation area.

9. A UAV hyperspectral imaging device, characterized in that: It includes an MCU, an IMU, a hyperspectral camera, a motor MX, and a motor MY; the IMU, the hyperspectral camera, the motor MX, and the motor MY are respectively connected to the MCU; The UAV hyperspectral imaging device further includes a program; after being executed by the MCU, the program can implement the UAV hyperspectral imaging method described in any one of claims 1 to 8 above.

10. A UAV hyperspectral imaging system, characterized in that: The invention comprises a drone and the drone hyperspectral imaging device as claimed in claim 9, which is arranged on the drone.

Images