Detection device, detection method, and control program

Abstract

To provide a detection device, detection method and control program which can highly accurately detect a withered tree region where a withered tree exists.SOLUTION: A detection device comprises: an image acquisition unit which acquires an optical image obtained by imaging a target region from the sky; an index calculation unit which calculates a vegetation index for each of a plurality of pixels included in the optical image; a setting unit which sets a plurality of section regions in the optical image; a dispersion degree calculation unit which calculates a dispersion degree of the vegetation index of each pixel included in each section region for each of the plurality of section regions; a detection unit which detects a withered tree region where a withered tree exists from the plurality of section regions on the basis of the dispersion degree; and an output unit which outputs information about a withered tree region.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a detection device, a detection method, and a control program. [Background technology]

[0002] In recent years, the number of trees to be inspected has been increasing due to the growing size and age of planted trees on the premises, as well as the increase in unmanaged trees, and there is a need to make inspection work more efficient.

[0003] Patent Document 1 describes a method for determining the health of trees in which the tree species estimated by analyzing satellite images is combined with the Normalized Difference Vegetation Index (NDVI) to determine the health of trees according to their species. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-144607 Summary of the Invention [Problem to be solved by the invention]

[0005] In tree inspection work, it is necessary to detect dead and damaged tree areas with high accuracy.

[0006] The present invention aims to provide a detection device, a detection method, and a control program that can detect dead and damaged tree areas where dead and damaged trees exist with high accuracy. [Means for solving the problem]

[0007] A detection device according to one aspect of the present invention is characterized by having an image acquisition unit that acquires an optical image of a target area taken from above; an index calculation unit that calculates a vegetation index for each of a plurality of pixels contained in the optical image; a setting unit that sets a plurality of divided areas within the optical image; a variability calculation unit that calculates, for each of the plurality of divided areas, the degree of variation in the vegetation index for each pixel contained in each of the plurality of divided areas; a detection unit that detects dead tree areas where dead trees exist from among the plurality of divided areas based on the degree of variation; and an output unit that outputs information regarding the dead tree areas.

[0008] In a detection device according to one aspect of the present invention, it is preferable that the detection unit detects, among a plurality of divided areas, divided areas in which the average value of the vegetation index of each pixel contained in each divided area is smaller than a first threshold value and the standard deviation of the vegetation index of each pixel contained in each divided area is greater than a second threshold value as dead tree areas.

[0009] In a detection device according to one aspect of the present invention, it is preferable that the index calculation unit calculates a GSI index for each of a plurality of pixels included in the optical image, and further includes an extraction unit that extracts a vegetation area from the optical image based on the difference between the vegetation index and the GSI index, and that the setting unit sets a plurality of divided areas within the vegetation area.

[0010] A detection method according to one aspect of the present invention is characterized in that a computer acquires an optical image of a target area taken from above, calculates a vegetation index for each of multiple pixels contained in the optical image, sets multiple divided areas within the optical image, calculates the degree of variation in the vegetation index for each pixel contained in each of the multiple divided areas, detects dead and damaged tree areas where dead and damaged trees exist from among the multiple divided areas based on the degree of variation, and outputs information regarding the damaged tree areas.

[0011] A control program according to one aspect of the present invention is a control program for a computer having an output unit, which causes a detection device to acquire an optical image of a target area taken from above, calculate a vegetation index for each of a plurality of pixels contained in the optical image, set a plurality of divided areas within the optical image, calculate the degree of variation in the vegetation index for each pixel contained in each of the plurality of divided areas, detect dead and damaged tree areas where dead and damaged trees exist from among the plurality of divided areas based on the degree of variation, and output information regarding the damaged tree areas from the output unit. [Effects of the Invention]

[0012] According to the present invention, the detection device, detection method, and control program can detect dead and damaged tree areas where dead and damaged trees exist with high accuracy. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram illustrating an example of a schematic configuration of a detection device. [Figure 2] 10 is a flowchart illustrating an example of a process for detecting a dead or damaged tree region. [Figure 3A] FIG. 10 is a diagram illustrating an example of an RGB image. [Figure 3B] FIG. 10 is a diagram illustrating an example of a red band image. [Figure 4A] FIG. 10 is a diagram illustrating an example of a green band image. [Figure 4B] FIG. 10 is a diagram illustrating an example of a blue band image. [Figure 5A] FIG. 1 is a diagram illustrating an example of a near-infrared band image. [Figure 5B] FIG. 10 is a diagram illustrating an example of a vegetation index image. [Figure 6A] FIG. 10 is a diagram illustrating an example of a GSI index image. [Figure 6B] FIG. 10 is a schematic diagram for explaining a vegetation area. [Figure 7A] FIG. 10 is a schematic diagram for explaining a segmented region. [Figure 7B] FIG. 10 is a diagram illustrating an example of a variation degree image. [Figure 8]FIG. 10 is a diagram illustrating an example of a representative value image. DETAILED DESCRIPTION OF THE INVENTION

[0014] Various embodiments of the present invention will be described below with reference to the drawings. Please note that the technical scope of the present invention is not limited to these embodiments, but extends to the inventions described in the claims and their equivalents.

[0015] 1 is a diagram showing an example of the schematic configuration of a detection device 1. The detection device 1 is an example of a computer, and detects dead and damaged tree areas where dead and damaged trees exist. The detection device 1 includes a memory unit 11, a communication unit 12, a display unit 13, an operation unit 14, and a processing unit 15.

[0016] The storage unit 11 stores programs or data. The storage unit 11 includes, for example, a semiconductor memory device. The storage unit 11 stores an operating system program, a driver program, an application program, data, and the like used for processing by the processing unit 15. Programs are installed into the storage unit 11 from a computer-readable, non-transitory, portable storage medium such as a CD (Compact Disc)-ROM (Read Only Memory) or a DVD (Digital Versatile Disc)-ROM using a known setup program or the like.

[0017] The communication unit 12 is an example of an output unit. The communication unit 12 enables the detection device 1 to communicate with other devices. The communication unit 12 includes a communication interface circuit. The communication interface circuit included in the communication unit 12 is a communication interface circuit such as a wired LAN (Local Area Network) or a wireless LAN. The communication unit 12 receives data from other devices and supplies the data to the processing unit 15, and also transmits data supplied from the processing unit 15 to other devices.

[0018] The display unit 13 is an example of an output unit. The display unit 13 displays an image. The display unit 13 includes, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 13 displays an image based on display data supplied from the processing unit 15.

[0019] The operation unit 14 accepts input operations by the user to the detection device 1. The operation unit 14 includes, for example, a keypad, a keyboard, or a mouse. The operation unit 14 may also include a touch panel integrated with the display unit 13. The operation unit 14 generates a signal according to the input operation by the user and supplies the signal to the processing unit 15.

[0020] The processing unit 15 is a device that comprehensively controls the operation of the detection device 1 and includes one or more processors and their peripheral circuits. The processing unit 15 includes, for example, a central processing unit (CPU). The processing unit 15 may also include a graphics processing unit (GPU), a digital signal processor (DSP), a large scale integration (LSI), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc. The processing unit 15 controls the operation of each component and executes various processes so that the various processes of the detection device 1 are executed in an appropriate order based on the programs stored in the storage unit 11 and inputs from the communication unit 12 and the operation unit 14.

[0021] The processing unit 15 includes, as functional blocks, an image acquisition unit 151, an index calculation unit 152, an extraction unit 153, a setting unit 154, a variation degree calculation unit 155, a detection unit 156, and an output control unit 157. Each of these units is a functional module realized by a program executed by the processing unit 15. Each of these units may be implemented in the detection device 1 as firmware.

[0022] 2 is a flowchart showing an example of the dead and damaged tree region detection process. The dead and damaged tree region detection process is executed mainly by the processing unit 15 in cooperation with each element of the detection device 1 based on a program stored in advance in the storage unit 11.

[0023] First, the image acquisition unit 151 acquires an optical image of a target area taken from above from an external information processing device (for example, an information processing device mounted on an optical satellite) via the communication unit 12 (step S101). The target area is an area that includes trees such as a forest. The detection device 1 inspects the trees included in the target area and detects dead and damaged tree areas where dead trees exist from the target area.

[0024] The optical image is an image such as a Pleiades optical satellite image. The optical image is an image in which the intensity of reflected light (electromagnetic waves) from sunlight at each position within a target area and the intensity of emitted light (electromagnetic waves) from each position are used as pixel values ​​for each pixel corresponding to each position, as sensed by an optical sensor mounted on the optical satellite. The optical image includes multiple images generated for each wavelength band of the electromagnetic waves sensed by the optical sensor mounted on the optical satellite.

[0025] The optical image includes a red band image, a green band image, a blue band image, and a near-infrared band image. The red band image is an image in which the pixel value of each pixel is the light intensity in a band including red light wavelengths. The green band image is an image in which the pixel value of each pixel is the light intensity in a band including green light wavelengths. The blue band image is an image in which the pixel value of each pixel is the light intensity in a band including blue light wavelengths. The near-infrared band image is an image in which the pixel value of each pixel is the light intensity in a band including near-infrared light wavelengths.

[0026] The optical image further includes an RGB image. An RGB image is a color image in which the pixel value of each pixel is a 48-bit color value consisting of the pixel value (16 bits) of the corresponding pixel in the red band image, the pixel value (16 bits) of the corresponding pixel in the green band image, and the pixel value (16 bits) of the corresponding pixel in the blue band image. Here, the number of bits of the pixel value is just an example, and pixels having pixel values ​​of 8 bits or 12 bits may also be used depending on the specifications of the satellite, etc.

[0027] FIG. 3A shows an example of an RGB image 300 captured from above of a predetermined target area 301.

[0028] 3A, target area 301 included in RGB image 300 includes vegetation area 302, which includes trees, and non-vegetation area 303, which does not include trees. Vegetation area 302 also includes dead and damaged tree area 304, which includes dead and damaged trees, and non-dead and damaged tree area 305, which does not include dead and damaged trees. Non-vegetation area 303 includes soil area 306, where soil is exposed on the ground surface, such as a bare ground area, and non-soil area 307, where concrete or the like is present and soil is not exposed on the ground surface.

[0029] FIG. 3B shows an example of a red band image 310 captured from above the target area 301.

[0030] In the example shown in Figure 3B, pixels corresponding to positions where the intensity of emitted or reflected red light is greater are shown with a higher brightness (closer to white), and pixels corresponding to positions where the intensity of emitted or reflected red light is smaller are shown with a lower brightness (closer to black).

[0031] FIG. 4A shows an example of a green band image 400 captured from above the target area 301.

[0032] In the example shown in FIG. 4A, pixels corresponding to positions where the intensity of emitted or reflected green light is greater are shown with a higher brightness (closer to white), and pixels corresponding to positions where the intensity of emitted or reflected green light is smaller are shown with a lower brightness (closer to black).

[0033] FIG. 4B shows an example of a blue band image 410 captured from above the target area 301.

[0034] In the example shown in Figure 4B, pixels corresponding to positions where the intensity of emitted or reflected blue light is greater are shown with a higher brightness (closer to white), and pixels corresponding to positions where the intensity of emitted or reflected blue light is smaller are shown with a lower brightness (closer to black).

[0035] FIG. 5A shows an example of a near-infrared band image 500 captured from above the target area 301.

[0036] In the example shown in FIG. 5A, pixels corresponding to positions where the intensity of emitted or reflected near-infrared light is greater are shown with higher brightness (closer to white), and pixels corresponding to positions where the intensity of emitted or reflected near-infrared light is smaller are shown with lower brightness (closer to black).

[0037] Next, the index calculation unit 152 calculates a vegetation index for each of multiple pixels included in the optical image from the optical image (step S102). The vegetation index is an index for understanding the state of vegetation in the target area, and indicates the amount or vitality of plants. The index calculation unit 152 calculates a Wide Dynamic Range Vegetation Index (WDRVI) as the vegetation index. The WDRVI has a higher value as the amount or vitality of plants increases, and a lower value as the amount or vitality of plants decreases. The index calculation unit 152 calculates the vegetation index for each pixel using the following formula (1) based on the red band image and the near-infrared band image. WDRVI=(αIR-R) / (αIR+R) …(1)

[0038] Here, α is a correction coefficient, and is set to a value within a range of, for example, 0.1 to 0.2. IR is the pixel value of the corresponding pixel in the near-infrared band image, i.e., the intensity of near-infrared light at the position corresponding to that pixel. R is the pixel value of the corresponding pixel in the red band image, i.e., the intensity of red light at the position corresponding to that pixel. WDRVI has a value within a range of -1.0 to +1.0. It is known that plants have a high reflectance of near-infrared light and a low reflectance of red light due to the action of chlorophyll. It is also known that as plants weaken, the activity of chlorophyll slows, resulting in a decrease in the reflectance of near-infrared light. Therefore, the detection device 1 can improve the detection accuracy of vegetation areas by using the WDRVI calculated by the above formula (1) as a vegetation index.

[0039] The index calculation unit 152 may calculate a Normalized Difference Vegetation Index (NDVI) as a vegetation index instead of the WDRVI. In this case, the index calculation unit 152 calculates the vegetation index of each pixel using the following formula (2) based on the red band image and the near-infrared band image. NDVI = (IR-R) / (IR+R) …(2)

[0040] The WDRVI is a vegetation index that was improved to alleviate the problem of NDVI value saturation in areas with a high Leaf Area Index (LAI). The LAI is an index that represents the amount of leaves in a plant community, and the more leaves there are, the higher the LAI value. By using the WDRVI as a vegetation index, the detection device 1 can further improve the accuracy of detecting vegetation areas compared to using the NDVI as a vegetation index.

[0041] The index calculation unit 152 generates a vegetation index image in which the pixel value of each pixel is used as a vegetation index.

[0042] FIG. 5B shows an example of a vegetation index image 510 generated from the red band image 310 and the near-infrared band image 500 using WDRVI as the vegetation index.

[0043] In the example shown in Fig. 5B, pixels with a higher vegetation index are shown with a higher brightness (closer to white), and pixels with a lower vegetation index are shown with a lower brightness (closer to black). As shown in Fig. 5B, in an area 511 corresponding to the target area 301, an area 512 corresponding to the vegetation area 302 has a high vegetation index, and an area 513 corresponding to the non-vegetation area 303 has a low vegetation index. Therefore, by using the WDRVI as a vegetation index, the detection device 1 can accurately distinguish between vegetation areas and non-vegetation areas.

[0044] Of the region 512 corresponding to the vegetation region 302, the region 515 corresponding to the non-dead tree region 305 has a high vegetation index overall, while the region 514 corresponding to the dead tree region 304 has a slightly low vegetation index in some areas. Also, of the region 513 corresponding to the non-vegetation region 303, the region 517 corresponding to the non-soil region 307 has a sufficiently low vegetation index, while the region 516 corresponding to the soil region 306 has a slightly high vegetation index.

[0045] Next, the index calculation unit 152 calculates a GSI (Grain Size Index) for each of the multiple pixels included in the optical image from the optical image (step S103). The GSI index, also known as a grain size index, is an index for detecting bare ground areas. The higher the GSI index value, the more likely it is that the position corresponding to that pixel is included in a bare ground area. The index calculation unit 152 calculates the GSI index for each pixel using the following formula (3) based on the red band image, green band image, and blue band image. GSI index=(RB) / (R+G+B) …(3)

[0046] Here, G is the pixel value of the corresponding pixel in the green band image, i.e., the intensity of green light at the position corresponding to that pixel. B is the pixel value of the corresponding pixel in the blue band image, i.e., the intensity of blue light at the position corresponding to that pixel. Like the WDRVI, the GSI index has a value within a range of -1.0 or more and +1.0 or less.

[0047] The index calculation unit 152 generates a GSI index image in which the pixel value of each pixel is used as the GSI index.

[0048] FIG. 6A shows an example of a GSI index image 600 generated from the red band image 310, the green band image 400, and the blue band image 410.

[0049] In the example shown in Fig. 6A, pixels with a higher GSI index are displayed with a higher brightness (closer to white), and pixels with a lower GSI index are displayed with a lower brightness (closer to black). As shown in Fig. 7A, among regions 601 corresponding to the target region 301, a region 606 corresponding to the soil region 306 has a higher GSI index than a region 602 corresponding to the vegetation region 302 and a region 607 corresponding to the non-soil region 307. Therefore, by using the GSI index, the detection device 1 can accurately distinguish between soil regions and other regions.

[0050] Next, the index calculation unit 152 calculates the difference between the vegetation index and the GSI index for each of the plurality of pixels included in the optical image (step S104). The index calculation unit 152 generates an image in which the pixel value of each pixel is a difference value obtained by subtracting the pixel value of the corresponding pixel in the GSI index image from the pixel value of the corresponding pixel in the vegetation index image, as a difference index image between the vegetation index image and the GSI index image.

[0051] As described above, the vegetation index is an index for understanding the vegetation status in a target area, and has a high value in vegetated areas and a low value in non-vegetated areas. However, among non-vegetated areas, soil areas may include grasslands or farmlands, in which case the vegetation index also has a high value in the soil areas. The GSI index has a higher value in areas corresponding to soil areas than in other areas. Therefore, the difference obtained by subtracting the GSI index from the vegetation index maintains a high value close to the vegetation index in vegetated areas and has a value sufficiently low relative to the vegetation index in soil areas. Note that in non-soil areas, the vegetation index is low, so the difference obtained by subtracting the GSI index from the vegetation index has a low value. By correcting the vegetation index using the GSI index, which indicates the likelihood of a bare ground area, the index calculation unit 152 can reduce the possibility of extracting grasslands or farmlands as vegetated areas.

[0052] Next, the extraction unit 153 performs a binarization process on the difference index image generated by the index calculation unit 152 (step S105). The extraction unit 153 determines whether the pixel value of each pixel included in the difference index image is equal to or greater than a threshold. The threshold is determined, for example, by Otsu's binarization. The threshold may be set to the average value or median value of the pixel values ​​of each pixel included in the difference index image. The threshold may also be set to a fixed value in advance. The extraction unit 153 generates a binary image in which the pixel value of each pixel is set to a valid value when the pixel value of the corresponding pixel in the difference index image is equal to or greater than the threshold, and is set to an invalid value when the pixel value of the corresponding pixel in the difference index image is less than the threshold.

[0053] Next, the extraction unit 153 extracts a vegetation area from the optical image based on the difference between the vegetation index and the GSI index calculated by the index calculation unit 152 (step S106). The extraction unit 153 performs processing such as labeling on the binary image and extracts an area consisting of adjacent valid pixels as a valid area. The extraction unit 153 generates data indicating the coordinates of the extracted valid area as vegetation area polygon data, and extracts an area in the optical image that corresponds to the vegetation area polygon data as a vegetation area.

[0054] FIG. 6B is a schematic diagram for explaining the vegetation area.

[0055] An image 610 shown in Fig. 6B is the same as the RGB image 300 shown in Fig. 3A. In the image 610, an area 612 indicates an area corresponding to the vegetation area 302 extracted by the extraction unit 153. As shown in Fig. 6B, each area corresponding to the vegetation area 302 is extracted as the vegetation area 612 by the extraction unit 153. In this way, the detection device 1 can detect the vegetation area with high accuracy by utilizing the difference between the vegetation index and the GSI index.

[0056] Furthermore, since processing is subsequently performed only on this vegetation area, the detection device 1 can speed up the dead tree area detection process. Note that if the extraction unit 153 has previously acquired land use status from, for example, a high-resolution land use and land cover map of Japan provided by JAXA, the extraction unit 153 may extract vegetation areas based on that use status.

[0057] Next, the setting unit 154 sets a plurality of segmented regions in the optical image (step S107). The setting unit 154 sets a plurality of segmented regions by dividing the vegetation region extracted by the extraction unit 153 in the optical image into a plurality of regions. The setting unit 154 sets each region including three or more pixels in each of the horizontal and vertical directions in the optical image as a segmented region. This enables the detection device 1 to accurately calculate the degree of variation and representative value of the vegetation index in each segmented region in the processing described below. The setting unit 154 sets square regions including three pixels in each of the horizontal and vertical directions in the optical image as segmented regions.

[0058] The setting unit 154 may set rectangular regions with different horizontal and vertical sizes as segmented regions. The setting unit 154 may also set regions with other shapes, such as circular regions or triangular regions, as segmented regions.

[0059] FIG. 7A is a schematic diagram for explaining the divided regions.

[0060] Image 700 shown in Fig. 7A is the same image as image 610 shown in Fig. 6B. In image 700, region 701 indicates a region corresponding to a segmented region set by setting unit 154. As shown in Fig. 7A, region 612 corresponding to vegetation region 302 extracted by extraction unit 153 is divided into a plurality of rectangular regions, thereby setting a plurality of segmented regions 701.

[0061] Next, the variation degree calculation unit 155 calculates the degree of variation in the vegetation index of each pixel included in each of the multiple divided areas (step S108). The variation degree calculation unit 155 calculates the standard deviation of the vegetation index of each pixel included in each divided area as the degree of variation. Note that the variation degree calculation unit 155 may also calculate other indices such as variance and half-width (the width of the peak where the peak value of the vegetation index distribution is half) as the degree of variation. The variation degree calculation unit 155 generates a variation degree image in which the pixel value of each pixel is used as the degree of variation of the divided area including the pixel corresponding to each pixel.

[0062] 7B shows an example of a variation degree image 710 generated from the red band image 310 and the near-infrared band image 500, with the standard deviation used as the degree of variation. Note that in the variation degree image 710 shown in FIG. 7B, for areas other than an area 712 corresponding to the vegetation area 302, the corresponding areas in the RGB image 300 are shown in order to improve visibility.

[0063] In the example shown in Figure 7B, pixels with a greater degree of variation are shown with a higher brightness (closer to white), and pixels with a smaller degree of variation are shown with a lower brightness (closer to black). As shown in Figure 7B, within region 712 corresponding to vegetation region 302, region 714 corresponding to dead tree region 304 has a greater degree of variation than region 715 corresponding to non-dead tree region 305.

[0064] As described above, within the region 712 corresponding to the vegetation region 302, the region 715 corresponding to the non-dead tree region 305 has a high vegetation index overall, while the region 714 corresponding to the dead tree region 304 has a slightly low vegetation index in some areas. However, the difference in the vegetation index value itself between the region 715 corresponding to the non-dead tree region 305 and the region 714 corresponding to the dead tree region 304 is small, making it difficult to distinguish between the dead tree region 304 and the non-dead tree region 305 based on the vegetation index value itself. However, the detection device 1 can accurately distinguish between dead tree regions and non-dead tree regions by utilizing the degree of variation in the vegetation index.

[0065] Next, the variation degree calculation unit 155 calculates, for each of the plurality of divided regions, a representative value of the vegetation index of each pixel included in each divided region (step S109). The variation degree calculation unit 155 calculates the average value of the vegetation index of each pixel included in each divided region as the representative value.

[0066] The variation degree calculation unit 155 may calculate other indexes such as a maximum value, a minimum value, a median value, etc. as the representative value. The variation degree calculation unit 155 generates a representative value image in which the pixel value of each pixel is used as the representative value of the partitioned region that includes the pixel corresponding to each pixel.

[0067] Fig. 8 shows an example of a representative value image 800 generated from the red band image 310 and the near-infrared band image 500, using the average value as the representative value. Note that in the representative value image 800 shown in Fig. 8, for areas other than the area 802 corresponding to the vegetation area 302, the corresponding areas in the RGB image 300 are shown in order to improve visibility.

[0068] In the example shown in Figure 8, pixels with smaller representative values ​​are displayed with higher brightness (closer to white), and pixels with larger representative values ​​are displayed with lower brightness (closer to black). As shown in Figure 8, among regions 802 corresponding to vegetation region 302, region 804 corresponding to dead tree region 304 tends to have a smaller representative value than region 805 corresponding to non-dead tree region 305. By using the representative values ​​of the vegetation indices in addition to the degree of variation in the vegetation indices, detection device 1 can distinguish between dead tree regions and non-dead tree regions with higher accuracy.

[0069] Next, the detection unit 156 detects dead and damaged tree regions where dead and damaged trees exist from among the multiple divided regions based on the degree of variation and the representative value calculated by the variation degree calculation unit 155 (step S110). The detection unit 156 detects, from among the multiple divided regions, divided regions in which the representative value of the vegetation index of each pixel included in each divided region is smaller than a first threshold value and the degree of variation of the vegetation index of each pixel included in each divided region is greater than a second threshold value, as dead and damaged tree regions.

[0070] For example, the detection unit 156 detects, among multiple divided areas, a divided area in which the average value of the vegetation index of each pixel contained in each divided area is smaller than a first threshold value and the standard deviation of the vegetation index of each pixel contained in each divided area is greater than a second threshold value as a dead tree area.

[0071] The first threshold is set, for example, in a preliminary experiment, to a value between the average or maximum of the representative values ​​calculated for dead tree regions included in various optical images and the average or minimum of the representative values ​​calculated for non-dead tree regions. The second threshold is set, for example, in a preliminary experiment, to a value between the average or minimum of the degree of variation calculated for dead tree regions included in various optical images and the average or maximum of the degree of variation calculated for non-dead tree regions.

[0072] On the other hand, the detection unit 156 detects, among the multiple divided areas, divided areas in which the representative value of the vegetation index of each pixel contained in each divided area is equal to or greater than a first threshold, and divided areas in which the degree of variation of the vegetation index of each pixel contained in each divided area is equal to or less than a second threshold, as non-dead tree areas.

[0073] Typically, in an area containing multiple trees, it is unlikely that multiple trees will die at the same time, but rather that multiple trees will die one after the other. Therefore, a segmented area containing dead trees is likely to also contain healthy trees, and the vegetation index tends to vary more than a segmented area containing only healthy trees. Therefore, the detection device 1 can appropriately detect dead tree areas through image processing by utilizing the degree of variation in the vegetation index.

[0074] In addition, in an area of ​​a certain size, the vegetation index will be small for dead trees, but will be large for healthy trees. By using the representative value of the vegetation index in a divided area of ​​a certain size, the detection device 1 can accurately detect a divided area in which only a portion of dead trees exists as a dead tree area.

[0075] For example, even if the representative value of a vegetation index is large, if the degree of variation in the vegetation index is large, there is a high possibility that dead and healthy trees are mixed. By using both the degree of variation and the representative value, the detection device 1 can improve the accuracy of detecting dead and damaged tree areas.

[0076] The detection unit 156 may calculate an evaluation value for each of a plurality of divided areas from the degree of variation and the representative value of the vegetation index, and detect dead tree areas based on the calculated evaluation value. The detection unit 156 calculates an evaluation value such that the greater the degree of variation of the vegetation index, the larger the evaluation value, and the smaller the representative value of the vegetation index, the larger the evaluation value.

[0077] The detection unit 156 detects, among the multiple segmented regions, those with evaluation values ​​greater than a threshold as dead tree regions, and those with evaluation values ​​equal to or less than the threshold as non-dead tree regions. The threshold is set, for example, in a preliminary experiment, to a value between the average or minimum evaluation value calculated for dead tree regions contained in various optical images and the average or maximum evaluation value calculated for non-dead tree regions. In this case, too, the detection unit 156 can accurately detect dead tree regions.

[0078] The detection unit 156 may also change the second threshold for comparison with the degree of variation in the vegetation index for each of the multiple divided areas based on the representative value of the vegetation index, and determine whether each divided area is a dead tree area based on whether the degree of variation in the vegetation index is greater than the second threshold. In this case, the detection unit 156 may set the second threshold to be smaller as the representative value of the vegetation index becomes smaller, making it easier to determine that each divided area is a dead tree area.

[0079] The detection unit 156 may change the first threshold for comparison with the representative value of the vegetation index for each of the multiple divided areas based on the degree of variation in the vegetation index, and determine whether each divided area is a dead or damaged tree area based on whether the representative value of the vegetation index is smaller than the first threshold. In this case, the detection unit 156 increases the first threshold as the degree of variation in the vegetation index increases, making it easier to determine that each divided area is a dead or damaged tree area. In these cases, the detection unit 156 can also detect dead or damaged tree areas with high accuracy.

[0080] The detection unit 156 may classify the sectional areas into groups using a known numerical classification technique, and determine whether each sectional area group is a dead tree area. In this case, the variation degree calculation unit 155 classifies, for example, sectional areas that are adjacent to each other and have similar representative values ​​of vegetation indices (the difference between the representative values ​​is equal to or less than a predetermined value) into the same group.

[0081] The variation degree calculation unit 155 may set a predetermined value using, for example, natural classification technology, so that adjacent divided areas with a relatively large difference in the representative value of the vegetation index are classified into different groups. The variation degree calculation unit 155 calculates the variation degree and representative value of the vegetation index for each classified divided area group. The detection unit 156 collectively detects, as dead tree areas, divided area groups among the multiple divided area groups, in which the representative value of the vegetation index of each pixel included in each divided area group is smaller than a first threshold and the variation degree of the vegetation index of each pixel included in each divided area group is greater than a second threshold.

[0082] The detection unit 156 can detect dead and damaged tree regions all at once, thereby reducing the processing time required for the dead and damaged tree region detection process. In addition, the detection unit 156 can reduce the number of dead and damaged tree region detection errors or missed detections due to the influence of noise in the image, etc.

[0083] Next, the output control unit 157 outputs information about the dead and damaged tree regions (step S111), completing the series of dead and damaged tree region detection processes. The output control unit 157 outputs information about the dead and damaged tree regions detected by the detection unit 156 by displaying it on the display unit 13. The output control unit 157 may also output information about the dead and damaged tree regions by transmitting it to an external information processing device via the communication unit 12. The output control unit 157 may highlight the dead and damaged tree regions on an RGB image, for example, by displaying them in a different color from other regions or by surrounding them with a thick line, thereby generating an image in which the dead and damaged tree regions are displayed so as to be distinguishable from other regions, and output this image as information about the dead and damaged tree regions. The output control unit 157 may also output the latitude or longitude indicating the dead and damaged tree regions as information about the dead and damaged tree regions.

[0084] The processing of step S103 may be omitted, and in step S106, the extraction unit 153 may extract the vegetation area using only the vegetation index image without using the GSI index image. Also, in step S106, the extraction unit 153 may extract the vegetation area using only the GSI index image without using the vegetation index image. Also, the processing of steps S103 to S106 may be omitted, and in step S107, the setting unit 154 may set a plurality of divided areas in the entire area of ​​the optical image. Also, the processing of step S109 may be omitted, and in step S110, the setting unit 154 may detect the dead tree area using only the degree of variation in the vegetation index without using the representative value of the vegetation index.

[0085] As described above in detail, the detection device 1 detects dead and damaged tree areas based on the degree of variation in vegetation indices in each divided area. This allows the detection device 1 to detect, as dead and damaged tree areas, divided areas where only some trees are dead and damaged, or divided areas that contain trees that are not completely dead but are weakened. Therefore, the detection device 1 can detect dead and damaged tree areas where dead and damaged trees exist with high accuracy.

[0086] Furthermore, the detection device 1 automatically detects dead and damaged tree regions through image processing, allowing users to efficiently inspect trees. Therefore, the detection device 1 can improve user convenience.

[0087] It should be understood by those skilled in the art that various changes, substitutions, and alterations can be made to the present invention without departing from the spirit and scope of the present invention. For example, the processes of the above-described parts may be executed in a different order as appropriate within the scope of the present invention. Furthermore, the above-described embodiments and modifications may be combined as appropriate within the scope of the present invention. [Explanation of symbols]

[0088] 1. Detection device 151 Image acquisition unit 152 Indicator calculation section 153 Extraction part 154 Settings 155 Variation degree calculation unit 156 Detector 157 Output control section

Claims

1. an image acquisition unit that acquires an optical image of a target area taken from above; an index calculation unit that calculates a vegetation index for each of a plurality of pixels included in the optical image; a setting unit that sets a plurality of segmented regions within the optical image; a variation degree calculation unit that calculates a degree of variation in the vegetation index of each pixel included in each of the plurality of divided areas; a detection unit that detects a dead tree region where a dead tree exists from among the plurality of divided regions based on the degree of variation; an output unit that outputs information about the dead tree region; A detection device comprising:

2. 2. The detection device according to claim 1, wherein the detection unit detects, among the plurality of divided areas, a divided area in which the average value of the vegetation index of each pixel included in each divided area is smaller than a first threshold value and the standard deviation of the vegetation index of each pixel included in each divided area is greater than a second threshold value as the dead tree area.

3. the index calculation unit calculates a GSI index for each of a plurality of pixels included in the optical image; an extractor configured to extract a vegetation area from the optical image based on a difference between the vegetation index and the GSI index; The detection device according to claim 1 , wherein the setting unit sets the plurality of divided regions within the vegetation region.

4. By computer, Acquire optical images of the target area from above, calculating a vegetation index for each of a plurality of pixels included in the optical image; A plurality of partitioned regions are set within the optical image; calculating a degree of variation in the vegetation index for each pixel included in each of the plurality of divided areas; detecting a dead tree region in which a dead tree exists from among the plurality of divided regions based on the degree of variation; outputting information about the dead tree region from an output unit; A detection method characterized by:

5. A control program for a computer having an output unit, Acquire optical images of the target area from above, calculating a vegetation index for each of a plurality of pixels included in the optical image; A plurality of partitioned regions are set within the optical image; calculating a degree of variation in the vegetation index for each pixel included in each of the plurality of divided areas; detecting a dead tree region in which a dead tree exists from among the plurality of divided regions based on the degree of variation; outputting information about the dead tree region from the output unit; A control program that causes a detection device to execute the above.

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