KR20200084972A - Method for acquisition of hyperspectral image using an unmanned aerial vehicle - Google Patents

Abstract

The present invention provides a method for acquiring a hyperspectral image. The method comprises: (a) a step of setting, by a flight path setting module (220), the flight path of an unmanned aerial vehicle (100) by using a collection area; (b) a step of acquiring, by the unmanned aerial vehicle (100), hyperspectral information for each preset image unit through line scanning by using a hyperspectral sensor (140) while flying along the set flight path; (c) a step of correcting, by a geometry correction module (250), the geometry of the hyperspectral information acquired for each image unit by using position information of each pixel; and (d) a step of acquiring, by an image matching module (260), a hyperspectral image of the collection area by matching the hyperspectral information acquired for each image unit, on which the geometric correction has been performed. Therefore, the method can solve a problem for which the accuracy of image matching is reduced by the characteristics of line scanning of the hyperspectral sensor.

Description

Method for acquisition of hyperspectral image using an unmanned aerial vehicle

The present invention relates to a method of acquiring a hyperspectral image using an unmanned aerial vehicle.

The general image sensor included in the camera detects only the information in the visible region, whereas when using a hyperspectral sensor, it can secure hundreds of times more information, including the ultraviolet region and infrared region. have. Therefore, when using a camera including a hyperspectral sensor, it is possible to secure a hyperspectral image including various information of a corresponding area, so that land cover, vegetation, and water quality can be accurately identified.

However, there are various problems in the image acquisition method using the existing hyperspectral sensor.

In order to secure a wide-spectrum hyperspectral image, it is common to shoot on manned vehicles, satellites, etc. In this case, the cell size is excessively large, such as 10 m to 50 m, to make precise measurement impossible. For example, when measuring the water depth, the precision decreases because it is measured in 100 m ² units from the surface.

To solve this, attempts were made to mount a hyperspectral sensor on an unmanned aerial vehicle such as a drone capable of flying at a short distance. In this case, the precision increases due to the small cell size, but another problem arises in that the accuracy decreases due to problems such as shaking during flight of the unmanned aerial vehicle. To prevent this, it is possible to consider the mounting of a high performance gimbal capable of correcting the shaking, but the weight of the high performance gimbal is considerably large, which has weakened the drone's flight performance.

On the other hand, since the hyperspectral sensor basically performs line scanning to secure hyperspectral information, software that matches a plurality of hyperspectral information is essential. However, the existing hyperspectral information matching software lacks its accuracy, and thus, it is possible to provide information at a point where a plurality of hyperspectral information-matched images are slightly deviated from the actual shooting point.

Review related patent documents.

Korean Patent Registration No. 10-1619836 discloses a drone using a gimbal and a shock absorber for a camera for ultra-spectral imaging. It is an advantage in that an image that compensates for the shaking of the drone is secured, but it does not disclose an image matching technique by line scanning.

Korean Patent Registration No. 10-1806488 discloses a technique for identifying a cancer body by mounting a hyperspectral image sensor on a drone. Korean Registered Patent No. 10-1744662 discloses a technique for classifying plants using hyperspectral images. To this end, hyperspectral image standard data for various cancer bodies or plants should be secured in advance, and an object to be photographed is identified by comparing the captured image with this. However, it does not solve the original problems identified at the shooting stage, such as the problem of deterioration in accuracy due to the shaking of the drone and the problem of increasing the efficiency of image registration.

Korean Patent Registration No. 10-1689197 discloses a gimbal assembly that can be attached to a drone. The assembly disclosed herein is composed of a first unit capable of rotation/reverse rotation, a second unit capable of elevation, and a third unit rotating them to compensate for the shaking of the vehicle. However, if it is configured as described above, the weight of the gimbal will increase excessively, resulting in a problem that the flying performance of the drone is lowered.

Korean Patent Publication No. 10-2014-0014819 discloses a method of identifying data in the form of an artificial intelligence algorithm by applying an index/query technique to data identified in hyperspectral images. The prerequisite for the use of this method is the accuracy of hyperspectral images, but it does not even suggest a method of increasing the accuracy of images captured in an environment such as an unmanned aerial vehicle.

U.S. Patent No. 7,181,055 proposes an algorithm for matching hyperspectral images. Here, the matching accuracy is increased by using a reflectivity image.

(Patent Document 1) Korean Registered Patent No. 10-1619836

(Patent Document 2) Korean Registered Patent No. 10-1806488

(Patent Document 3) Korean Registered Patent No. 10-1744662

(Patent Document 4) Korean Registered Patent No. 10-1689197

(Patent Document 5) Korean Patent Publication No. 10-2014-0014819

(Patent Document 6) U.S. Patent No. 7,181,055

The present invention was devised to solve the above problems.

Specifically, to solve the problem that the accuracy of image registration is lowered due to the characteristics of the line-scanning hyperspectral sensor, and to solve the problem of image shaking due to being photographed on an unmanned aerial vehicle.

One embodiment of the present invention for solving the above problems, (a) the flight path setting module 220, using the collection area to set the flight path of the unmanned air vehicle (100); (b) while the unmanned aerial vehicle 100 is flying along the set flight path, using the hyperspectral sensor 140, acquiring hyperspectral information for each preset image unit through line scanning; (c) a geometric correction module 250 performing geometric correction using location information of each pixel on the hyperspectral information acquired for each image unit; And (d) the image registration performing module 260, matching the hyperspectral information acquired for each image unit, in which the geometric correction has been performed, to obtain an ultraspectral image of the collection area, In the step (d), the (d11) correspondence point checking module 262 includes any one of the two hyperspectral information (A, B) acquired for each image unit in which the geometric correction has been performed. With respect to the XY coordinates (x, y) of one pixel of the information (A) and the pixel values (I(x, y)) at the coordinates, one pixel of the other hyperspectral information (B) The normalized correlation of the XY coordinates (x', y') and the pixel values (I(x', y')) at the coordinates is calculated, but all of the one of the hyperspectral information (A) is calculated. By calculating all the normal correlation coefficients of each of the pixels of the other hyperspectral information (B) for each pixel, the XY coordinates of the point where the value of the normal correlation coefficient is large are the two hyperspectral information (A, B). Checking with the corresponding point of; (d12) the image matching performing module 260 matching the two hyperspectral information (A, B) based on the identified corresponding point; And (d13) the image registration performing module 260 performs steps (d1) to (d2) for each pair of hyperspectral information for each image unit obtained in step (b), thereby matching the image. It provides a method for acquiring a hyperspectral image, comprising the step of acquiring a superspectral image of the collected area.

In addition, before the step (d11), the (d21) integral image calculation module 261, in the hyperspectral information obtained for each image unit in which the geometric correction is performed, for each pixel, one reference coordinate Further comprising the step of calculating the integral image (integral image) pixel value on the basis of, it is preferable that the pixel value in the step (d11) is the integral image pixel value calculated in the step (d21).

In addition, before the step (d21), the (d31) normalization module 240 sets the XY coordinates of the pixels of the hyperspectral information of any one image unit to an ordered pair (x, y) of the integer, and the set XY Calculate the average coordinates of the coordinates, and set the calculated average coordinates as the origin (0, 0) so that the unit distance between the sequence pairs (x, y) of each integer is √2, and the XY coordinates (x, y) It is preferable to further include the step of normalizing the hyperspectral information of any one of the above image units by moving X to normalized XY coordinates (x', y').

In addition, after the (d31) step, (d32) the normalization module 240 before normalization of any one of the hyperspectral information XY coordinates (x, y) and normalized XY coordinates (x', y') Calculating a homography (H) that is a determinant including parameters using the values of And (d33) using the calculated homography (H) to normalize the other hyperspectral information.

In addition, in step (d32), homogeneous coordinates are utilized, and the XY coordinates before normalization are referred to as (x, y, 1) as determinant A, and the normalized XY coordinates are (x', y', 1) is referred to as determinant A', and using the following equation but utilizing a number of XY coordinates, homography (H) is calculated, and step (d33), the other hyperspectral information to the determinant A Substituting, it is preferable that the determinant A'which is the normalized XY coordinate is calculated.

In addition, the unmanned aerial vehicle 100 is a drone, and the drone includes: a camera 130 equipped with the hyperspectral sensor 140; Gimbal 110; A shock absorber 120 provided between the gimbal 110 and the camera 130; And a communication unit 150 electrically connected to the camera 130, wherein the communication unit 150 transmits the hyperspectral information acquired by the hyperspectral sensor 140 by photographing the camera 130 to the outside. It is desirable to do.

In addition, the communication unit 150 transmits the hyperspectral information to the photographing image input module 230 through the communication module 210, and the photographing image input module 230 is transmitted to the geometric correction performing module 250. It is desirable to transmit the hyperspectral information.

Another embodiment of the present invention for solving the above problems is to provide a computer-readable recording medium in which the program is stored in a computer-readable recording medium to execute the above-described method and the code is recorded.

According to the method of the present invention, by matching super spectral information secured in a small image unit (line unit) with high accuracy, it is possible to obtain an excellent super spectral image with high accuracy and precision. The image thus secured can be used as useful information for checking various characteristics (vegetation, topography, etc.) in the terrain, as well as checking the depth.

It is possible to effectively correct the shaking of the unmanned aerial vehicle without using a high-performance, high-performance gimbal. Used in combination with the above-described image matching technology, it is possible to obtain an excellent hyperspectral image with high synergistic effect.

This method is computer-programmed, and when an observation value and a captured hyperspectral image of a plurality of points are input, a regression equation can be automatically determined, so that a fast regression calculation and a target object based thereon can be determined while maintaining high accuracy. It has the advantage of being identifiable. In particular, in the past, it was necessary to perform complicated calculations and data transfers to and from a large number of programs. However, all of these problems are solved, and an integral image can be used to reduce the calculation time that took several days by about 50% or more.

1 shows a system for carrying out a method according to the invention.
2 is a flow chart for explaining the method according to the present invention.
3 is a photograph for explaining an example of an unmanned aerial vehicle used in the method according to the present invention.
Figure 4 is a schematic diagram for explaining a method for acquiring hyperspectral information by an unmanned aerial vehicle in the method according to the present invention.
5 is a schematic diagram for explaining geometric correction in the method according to the present invention.
6 is a schematic diagram for explaining image registration in the method according to the present invention.
7 is a schematic diagram for explaining a method of using a normal correlation coefficient for image registration in the method according to the present invention.
8 is a schematic diagram for explaining a method of using an integral image in image registration in the method according to the present invention.
9 is a schematic diagram for explaining the normalization step in the method according to the present invention.

Hereinafter, "image acquisition" should be understood as a concept including any one or more of all processes for obtaining a final image by using image information such as photographing, processing, pre-processing, post-processing, and production.

Hereinafter, the present invention will be described in more detail with reference to the drawings.

System Description

The system in which the method according to the present invention is performed may be divided into an unmanned air vehicle 100 and an image processing unit 200. The unmanned air vehicle 100 may be, for example, a drone, but any type of air vehicle flying at a short distance from the ground surface may be used.

The unmanned aerial vehicle 100 has a gyro sensor and an acceleration sensor, and is provided in the gimbal 110 and the gimbal 110 that can move the camera 130 in the opposite direction of movement, and the shock absorber 120 absorbing shock. It includes a camera 130 equipped with the hyperspectral sensor 140, and a communication unit 150 capable of transmitting the hyperspectral information obtained from the hyperspectral sensor 140 to the image processing unit 200.

The gimbal 110 may be a gimbal of any structure that can be secured, but considering the weight, a 1, 2, or 3 axis gimbal is preferable to a 4 or 5 axis gimbal. However, the limit of the shaking compensation of the 1, 2, and 3 axis gimbals can be overcome by the compensation of the shock absorber 120.

In addition, the unmanned aerial vehicle 100 may further include a GPS sensor for checking location information.

An example of such an unmanned aerial vehicle 100 is shown in FIG. 3.

The image processing unit 200 includes a communication module 210, a flight path setting module 220, a captured image input module 230, a normalization performing module 240, a geometric correction performing module 250, and a video matching performing module 260. And a hyperspectral image output module 270.

The communication module 210 communicates with the communication unit 150 of the unmanned air vehicle 100 by wire or wirelessly, so that the captured image input module 230 receives the hyperspectral information obtained from the hyperspectral sensor 140. The received information may include location information.

The flight path setting module 220 performs a function of setting a flight path of the unmanned air vehicle 100 to secure the image based on a collection area for collecting hyperspectral information.

The normalization execution module 240 performs a normalization function of the secured hyperspectral information. In all geometric transformations, rotation, size, and preservation of parallelism occur based on the origin (0, 0), so normalization of each ordered pair of XY coordinates present in each image is required.

The geometric correction execution module 250 performs geometric correction to compensate for distortion caused by geometric conditions such as bending of the ground from the secured hyperspectral information.

The image registration performing module 260 performs a function of matching a plurality of secured hyperspectral information. To this end, an integral image calculation module 261 and a corresponding point confirmation module 262 are provided.

The hyperspectral image output module 270 performs a function of outputting the hyperspectral image obtained as the final result that is normalized, geometrically corrected, and image-matched.

The method of performing a specific function of each module will be described later.

Description of the method

Referring to FIGS. 2 and 4 to 9, a method for acquiring a hyperspectral image according to the present invention will be described.

First, the flight path setting module 220 sets a flight path of the unmanned air vehicle 100 using a collection area (S110). As the hyperspectral sensor 140 performs line scanning, as shown in FIG. 4, an angle of view of the unmanned air vehicle 100 with respect to a collection area that is an object to acquire hyperspectral information ), a flight path of the unmanned aerial vehicle 100 is established. The setting method may be any according to the prior art, and detailed description is omitted.

Next, the unmanned aerial vehicle 100 acquires hyperspectral information for each preset image unit through line scanning using the hyperspectral sensor 140 while flying along the set flight path (S120). Here, the image unit is preset by a manufacturer according to the characteristics of the camera 130 or the hyperspectral sensor 140, and the hyperspectral information may be obtained in different ways according to each characteristic.

With respect to the hyperspectral information identified in this way, geometric correction is performed (S140), and image registration is performed (S150), so that the final result, the hyperspectral image, is secured.

Explain the specific method.

First, the geometric correction is performed by the geometric correction execution module 250 (see FIG. 5). Specifically, geometric correction is performed using the location information of each pixel on the hyperspectral information acquired for each image unit. Any conventionally known method may be applied.

The following image registration is a process of matching a plurality of hyperspectral information using a constant reference point (see FIG. 6). At least two pairs of a plurality of hyperspectral information are photographed so as to overlap each other, a predetermined reference point is found, and two pairs of hyperspectral information are superimposed based on these reference points, and this process is performed for each pair of all hyperspectral information. By repeating at, the final hyperspectral image is obtained. That is, the basis of image registration is a process of superimposing two hyperspectral information.

In the present invention, the "correspondence point extraction algorithm" is devised, and image registration is performed accordingly. This is by extracting a template from one of the two hyperspectral information having overlapping regions and finding the highest correlation point in the other hyperspectral information from the extracted template. , It is an algorithm that sets the corresponding point as a reference point of overlap, that is, a matching point. This is illustrated in FIG. 7.

Specifically, image registration of two hyperspectral information (A, B) having overlapping regions (indicated by image1 and image2 in FIG. 7) will be described.

First, the XY coordinates (x, y) of one pixel of any one of the hyperspectral information (A) and the corresponding one of the two superspectral information (A, B) that the corresponding point confirmation module 262 overlaps With respect to the pixel values I(x, y) in the coordinates, the XY coordinates (x', y') of one pixel of the other hyperspectral information B and the pixel values I(( x', y')) to calculate the normalized correlation. This process, as will be described later, the XY coordinates (x') of all the pixels of the template of one of the hyperspectral information (A), each of the XY coordinates (x, y) of each pixel of the template of the hyperspectral information (A) , y').

The equation for calculating the normal normal correlation coefficient is the same as Equation 1 below, but in the present invention, Equation 2 using a pixel value is further used to secure higher accuracy.

The XY coordinates of the hyperspectral information A are represented by (x, y), and the XY coordinates of the hyperspectral information B are represented by (x', y'). The upper bar means the average value (this is the same for all the formulas below). n is the total number of pixels, I(x, y) is the pixel value corresponding to (x, y) on the XY coordinates, the pixel value of the hyperspectral information (A) is represented by I ₁ and the hyperspectral information (B) The pixel value of is represented by I ₂ . W is the horizontal pixel size of the template, and H is the vertical pixel size of the template.

In this way, if the normal correlation coefficient is calculated for every pixel coordinate on the template and expressed as XY coordinates, a correlation map as illustrated in the lower right of FIG. 7 can be calculated. The position having the maximum value in the correlation coefficient map, that is, the XY coordinate of the point where the value of the normal correlation coefficient is large is checked as a corresponding point of the two hyperspectral information (A, B).

Now, the image registration performing module 260 matches the two hyperspectral information (A, B) based on the corresponding point.

The image registration performing module 260 performs image matching by performing this process for each pair of all hyperspectral information for each image unit, thereby obtaining a superspectral image in which all the captured images are matched.

However, in this case, since a large number of calculations are performed, the calculation speed may be slow depending on the performance of the image processing unit 200. Therefore, in another embodiment of the present invention, in order to realize a faster calculation speed, it is possible to perform image registration in a state in which the computation amount is greatly reduced by using an integral image. Integral image is to set the reference coordinates, use the directionality based on this, sum the pixel values of each pixel, and use it as the pixel value required for normal correlation calculation as in Equation (2).

Referring to FIG. 8, the concept of using an integral image will be described. The left side shows a general image. The number recorded for each pixel is a pixel value. The right side shows the integral image using the left image. In the integral image here, it is an integral image formed by setting "2" in the upper left as reference coordinates and then summing each value in the left and bottom directions. For example, "13" in the second row and third row is a value of 2+1+5+0+3+2.

If you want to get the sum of the pixel values of the 9 pixels in the middle of the image, you need to perform a total of 9 operations in the general image on the left. However, in the integral image on the right, four operations are sufficient. That is, by generating an integral image, the computation amount is reduced to 50% or less.

Specifically, the method of using the integral image in the hyperspectral image is as follows. First, a new pixel value II(x, y) by an integral image is set for each pixel using Equation (3).

Each symbol is as shown in equation (2). Now, using this, the normal correlation coefficient described in Equation 2 can be calculated by Equation 4.

Here, the value of the standard deviation σ used in the denominator can be calculated through Equation (5). As mentioned above, I means the pixel value.

In this way, once the integral image is generated and placed, by using Equations 3 to 5, it is no longer necessary to use a large amount of computation of Equation 2 for each operation, so that the normal correlation coefficient can be quickly calculated. It can be preferred.

Meanwhile, as described above, in all geometric transformations, rotation, size, and preservation of parallelism occur based on the origin (0, 0), and thus, in one embodiment of the present invention, the XY coordinates of each image are After normalization of each sequence pair, geometric correction and image registration may be performed.

Specifically, the normalization module 240 sets the XY coordinates of the pixels of the hyperspectral information of any one image unit as an ordered pair (x, y) of integers, calculates the average coordinates of the set XY coordinates, and performs the calculation The XY coordinates (x, y) are normalized XY coordinates (x', y) so that the unit distance between the ordered pairs (x, y) of each integer is √2 by setting the averaged coordinates as the origin (0, 0). By moving to'), the hyperspectral information of any one of the image units is normalized. This concept is illustrated in FIG. 9.

More specifically, Equation (6) can be used for this operation.

Here, A is the XY coordinate in the hyperspectral information to be normalized (ie, not normalized), and A'is the normalized XY coordinate. It is preferable that the coordinates here use a homogeneous coordinate system for parameter calculation described later. That is, the XY coordinates are referred to as determinant A as (x, y, 1), and the normalized XY coordinates are referred to as determinant A'as (x', y', 1). It is expressed as a kind of vector. In other words, the (x, y) coordinate in the homogeneous coordinate system can be understood as a line composed of countless points.

H is homography, which is a matrix of parameters (h ₁₁ to h ₃₃ ) for normalization. That is, if the homography (H) is calculated, all hyperspectral information can be normalized easily.

For calculation of homography (H), Equation 6 is used, but any one normalized value is required. That is, any one of the hyperspectral information is separately normalized using the method described with reference to FIG. 9, and then the homography (H) is calculated using the value. For example, if four sequence pairs of x and y of the XY coordinates before normalization are known and the normalized values of the four sequence pairs are known, using Equation 6, Equation 7 and Equation 8 can be derived. Thereby, each parameter (h ₁₁ ~h ₃₃ ) can be confirmed accordingly.

When the homography (H) is calculated by Equation 8, XY coordinates of all pixels of all hyperspectral images are now substituted into Equation 6, so that it is possible to normalize with only a small amount of computation, without having to separately calculate the normalized value. Do.

As described above, the presence of homography (H) means that not only only coordinate changes between corresponding points are possible, but also all points existing in the coordinate system of two hyperspectral information can be converted to each other. Accordingly, if the other hyperspectral information is normalized based on any one of the two hyperspectral information, and the coordinate transformation is performed, the two hyperspectral images are moved to the same coordinate system.

As described above, the present specification has been described with reference to the embodiments shown in the drawings so that those skilled in the art can easily understand and reproduce the present invention, but these are merely exemplary, and those skilled in the art can make various modifications and equivalents from the embodiments of the present invention. It will be understood that embodiments are possible. Therefore, the protection scope of the present invention should be defined by the claims.

100: unmanned air vehicle
110: gimbal
120: shock absorber
130: camera
140: hyperspectral sensor
150: communication unit
200: image processing unit
210: communication module
220: flight path setting module
230: video recording module
240: normalization execution module
250: geometric correction execution module
260: image registration module
261: Integral image calculation module
262: correspondence point confirmation module
270: hyperspectral image output module

Claims (9)

(a) the flight path setting module 220, using the collection area to set the flight path of the unmanned air vehicle (100);
(b) the unmanned aerial vehicle 100 acquiring hyperspectral information for each preset image unit through line scanning using the hyperspectral sensor 140 while flying along the set flight path. ;
(c) a geometric correction module 250 performing geometric correction using location information of each pixel on the hyperspectral information acquired for each image unit; And
(d) the step of performing the image matching module 260, matching the hyperspectral information obtained for each image unit, in which the geometric correction has been performed, to obtain a superspectral image of the collection area,
Step (d) is,
(d11) The corresponding point confirmation module 262 in any two hyperspectral information (A, B) acquired for each image unit in which the geometric correction has been performed, any one of the hyperspectral information (A) The XY coordinates (x', y) of any one pixel of the other hyperspectral information (B) with respect to the XY coordinates (x, y) of the pixel and the pixel value (I(x, y)) of the corresponding coordinates ') and the normalized correlation of the pixel values (I(x', y')) at the corresponding coordinates are computed, but the other one for each pixel of any one of the hyperspectral information A Confirming the XY coordinates of the point where the value of the normal correlation coefficient is large as the corresponding point of the two hyperspectral information (A, B) by calculating all the normal correlation coefficients of each pixel of the hyperspectral information (B);
(d12) the image matching performing module 260 matching the two hyperspectral information (A, B) based on the identified corresponding point; And
(d13) The image registration performing module 260 performs steps (d11) to (d12) for each pair of hyperspectral information for each image unit obtained in step (b), and performs image registration. Acquiring a hyperspectral image of the collection area,
How to acquire hyperspectral images.
According to claim 1,
Before step (d11),
(d21) Integral image calculation module 261, in the hyperspectral information obtained for each image unit in which the geometric correction is performed, for each pixel, an integral image based on one reference coordinate. Further comprising the step of calculating the pixel value,
The pixel value in step (d11) is an integral image pixel value calculated in step (d21),
How to acquire hyperspectral images.
According to claim 2,
Before step (d21),
(d31) The normalization module 240 sets the XY coordinates of the pixels of the hyperspectral information of any one image unit as an ordered pair (x, y) of integers, calculates the average coordinates of the set XY coordinates, and calculates the The XY coordinates (x, y) are normalized XY coordinates (x', y) so that the unit distance between the ordered pairs (x, y) of each integer is √2 by setting the averaged coordinates as the origin (0, 0). By moving to'), further comprising the step of normalizing the hyperspectral information of any one of the image units,
How to acquire hyperspectral images.
The method of claim 3,
After step (d31),
(d32) The normalization module 240 uses the values of the XY coordinates (x, y) before normalization of any one of the hyperspectral information and the XY coordinates (x', y') after normalization. Calculating a contained determinant, homography (H); And
(d33) using the calculated homography (H), normalizing the other hyperspectral information,
How to acquire hyperspectral images.
The method of claim 4,
In step (d32), homogeneous coordinates are utilized, and the XY coordinates before normalization are referred to as determinant A as (x, y, 1), and the XY coordinates after normalization are (x', y', 1). It is referred to as determinant A', and uses the following formula, but utilizes multiple XY coordinates to calculate homography (H),

In the step (d33), another hyperspectral information is substituted into the determinant A, and the determinant A'which is the normalized XY coordinate is calculated.
How to acquire hyperspectral images.
The method according to any one of claims 1 to 5,
The drone 100 is a drone,
The drone is:
A camera 130 equipped with the hyperspectral sensor 140;
Gimbal 110;
A shock absorber 120 provided between the gimbal 110 and the camera 130; And
It includes a communication unit 150 electrically connected to the camera 130,
The communication unit 150 transmits the hyperspectral information acquired by the hyperspectral sensor 140 to the outside by photographing the camera 130,
How to acquire hyperspectral images.
The method of claim 6,
The communication unit 150 transmits the hyperspectral information to the photographing image input module 230 through the communication module 210, and the photographing image input module 230 transmits the hyperspectral to the geometric correction performing module 250. Sending information,
How to acquire hyperspectral images.
Stored in a computer-readable recording medium
A program for executing the method according to claim 1.
A computer-readable recording medium in which program code for executing a method according to any one of claims 1 to 7 is recorded.

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