JP2015141118A - Forest physiognomy analyzer, forest physiognomy analysis method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To use a color image to visualize a difference in forest physiognomy.SOLUTION: A laser measurement feature quantity extraction unit 20 extracts predetermined one or more laser measurement feature quantities on the basis of aerial laser measurement data obtained by scanning a target area including a forest. An aerial photographic image feature quantity extraction unit 22 extracts predetermined one or more aerial photographic image feature quantities on the basis of an aerial photographic image obtained by imaging the target area from the air. A color image generation unit 24 generates a color image of the target area on the basis of each set of the laser measurement quantities and the aerial photographic image feature quantities. The color image generation unit 24 determines a pixel value of the color image on the basis of values in an area that correspond to pixels of multiple imaging feature quantities respectively being the laser measurement quantities or the aerial photographic image feature quantities.

Description

  The present invention relates to a forest facies analysis apparatus, a forest facies analysis method, and a program for visualizing differences in forest fauna with color images from aerial images and aerial laser measurement data.

  The forest fauna is the state and form of the forest according to the tree species / age age, the canopy and the growth state of the tree, and the forest fauna compartment is a forest area divided by the forest fauna. In general, forest type maps are created by identifying the type of forest type (forest type) in the forest type section.

  Here, remote sensing from the sky by an aircraft or the like can grasp the situation on the ground in a wide range, and is used for analysis of forest fauna such as creation of forest fauna division maps. Specifically, conventionally, high-resolution image data from the sky such as aerial photographs has been mainly used for forest phase analysis. Extraction of forest facies sections and creation of forest facies division maps using the data are performed by humans by visual interpretation using a stereoscope or a digital mapper. However, the interpretation of forestry using aerial photography is affected by the timing of shooting, the direction and angle of shooting, the azimuth and altitude of the sun, and if you try to avoid it, shooting opportunities are limited. Have a problem. Also, for example, it may be difficult to distinguish forest fauna with different tree species by color, or to distinguish forest fauna with different age and growth status even from the same tree species from aerial photographs that do not include height information. Due to difficulties, it is not always easy to visually distinguish forest stands based on aerial photographs.

  Another remote sensing technology here is aviation laser measurement. Three-dimensional point cloud data acquired by aerial laser measurement is widely used for measurement of forest topography and tree height, and estimation of standing tree density and volume.

  Since the height information of trees can be obtained from the aerial laser measurement data, it has been studied to use the aerial laser measurement data or use it in combination with aerial photographs to automatically determine the forest fauna. However, the forest phase analysis that has been proposed in the past mainly uses simple information such as height and reflection intensity among the information that can be included in the aviation laser measurement data.

JP 2008-46837 A JP 2011-24471 A JP2013-54660A

  The aerial laser measurement is an active sensing that directly acquires the 3D structure information of the feature and irradiates the laser itself, so the conditions for collecting the 3D structure information data of the feature are gentler than the aerial photograph. It is. Therefore, by using aerial photographs and aerial laser measurements in combination, there is a possibility that forest facies analysis such as creation of forest facies division maps can be performed more stably and accurately. However, techniques for visual interpretation of forest facies or visual interpretation processing results using aerial images such as aerial photographs and satellite images and aerial laser measurement data have not been sufficiently studied. .

  Specifically, visual interpretation (visualization) is required to perform visual interpretation and visual confirmation work, but in the past, how to obtain information necessary for interpretation of forest phase from aerial images and aerial laser measurement data. There has not been sufficient study on whether to extract and visualize them. In particular, regarding visualization of forestry, it was not always sufficient to examine what information contained in aerial laser measurement data could be used to clearly express differences in forestry.

  An object of the present invention is to provide a forest facies analysis apparatus, a forest facies analysis method, and a program for visualizing a difference in forest facies in a color image by using an aerial image and aerial laser measurement data together.

  (1) The forest fauna analysis apparatus according to the present invention extracts a laser measurement feature amount that extracts one or more predetermined laser measurement feature amounts based on aerial laser measurement data acquired by scanning a target area including a forest. Means, an aerial photographed image feature quantity extracting means for extracting one or more predetermined aerial photographed image feature quantities based on an aerial photographed image obtained by photographing the target area from above, the laser measurement feature quantity and the aerial image A means for generating a color image of the target area based on a set with a captured image feature amount, each corresponding to a plurality of pixels of the imaging feature amount that is the laser measurement feature amount or the aerial captured image feature amount Color image generating means for determining pixel values of the color image based on values in the area.

  (2) In the forest phase analysis apparatus described in (1) above, the laser measurement feature quantity extraction unit reflects the reflection at each measurement point determined according to the number of reflected pulses with respect to the laser irradiation pulse as the laser measurement feature quantity. Extracting at least one of a pulse index, a laser reflection intensity, a texture feature amount extracted from a pattern obtained by binarizing an image in the target area using the reflection intensity of the laser as a pixel value, and a tree height, The aerial captured image feature amount extraction unit may extract at least one of texture information and spectrum information extracted from the aerial captured image as the aerial captured image feature amount.

  (3) In the forest phase analysis apparatus described in (1) and (2) above, the laser measurement feature amount extraction unit extracts at least a tree height as the laser measurement feature amount, and the color image generation unit includes the imaging The color image including the tree height in the feature amount can be generated.

  (4) In the forest fauna analysis apparatus described in (3) above, it has display means for displaying the plurality of color images in a comparable manner, and the color image generation means displays the plurality of color images displayed by the display means. As another example, the combination of the plurality of imaging feature amounts may be different from each other.

  (5) The forest fauna analysis method according to the present invention is a laser measurement feature extraction that extracts one or more predetermined laser measurement features based on aerial laser measurement data acquired by scanning a target area including a forest. A step of extracting a predetermined one or a plurality of aerial photographed image feature values based on an aerial photographed image obtained by photographing the target area from above, the laser measurement feature value and the aerial image A processing step of generating a color image of the target area based on a set with a captured image feature amount, each corresponding to a plurality of pixels of the imaging feature amount that is the laser measurement feature amount or the aerial captured image feature amount And a color image generation step for determining a pixel value of the color image based on a value in a region to be processed.

  (6) A program according to the present invention is a program for causing a computer to perform forest facies analysis, and is predetermined based on aerial laser measurement data acquired by scanning the target area including the forest. Laser measurement feature quantity extraction means for extracting one or a plurality of laser measurement feature quantities; aerial photography for extracting one or more predetermined aerial photographed image feature quantities based on an aerial photographed image obtained by photographing the target area from above Image feature quantity extraction means, and means for generating a color image of the target area based on a set of the laser measurement feature quantity and the aerial photographed image feature quantity, the laser measurement feature quantity or the aerial photography respectively A color image generator that determines a pixel value of the color image based on values in a region corresponding to pixels of a plurality of imaging feature amounts that are image feature amounts , To function as.

  According to the present invention, it is possible to visualize a difference in forest fauna in a color image by using an aerial photographed image and aerial laser measurement data in combination.

It is a block diagram which shows the structure of the outline of the forest facies analysis system which is embodiment of this invention. It is a flow chart of an outline of processing and data in a forest facies analysis system which is an embodiment of the present invention. It is a schematic diagram explaining the example of the feature-value obtained from aviation laser measurement data. It is an example of the aerial photograph (ortho image) of an object area. It is an example of a DCHM (Digital Canopy Height Model) image of the target area shown in FIG. 5 is an example of an RI (Reflection Intensity) image of the target area shown in FIG. 4. It is an example of the BRI (Binary Reflection Intensity) image of the target area shown in FIG. It is an example of the FPR (First Pulse Ratio: First Pulse Ratio) image of the object area shown in FIG. It is an example of the IPR (Intermediate Pulse Ratio) image of the target area shown in FIG. It is explanatory drawing which shows the example of image BI (binary image: Binary Image). It is explanatory drawing which shows the example of image BGI (binary gradient image: Binary Gradient Image). It is explanatory drawing which shows the example of a LBP (Local Binary Pattern: Local Binary Pattern) image.

  Hereinafter, embodiments of the present invention (hereinafter referred to as embodiments) will be described with reference to the drawings.

  FIG. 1 is a block diagram illustrating a schematic configuration of a forest phase analysis system 2 according to the embodiment. The system includes an arithmetic processing device 4, a storage device 6, an input device 8, and an output device 10. As the arithmetic processing unit 4, it is possible to make dedicated hardware for performing the processing of this system. However, in this embodiment, the arithmetic processing unit 4 is constructed using a computer and a program executed on the computer. Is done.

  The arithmetic processing unit 4 includes a CPU (Central Processing Unit) of a computer and functions as a laser measurement feature amount extraction unit 20, an aerial captured image feature amount extraction unit 22, and a color image generation unit 24, which will be described later.

  The storage device 6 is a memory device such as a ROM (Read Only Memory) or a RAM (Random Access Memory). The storage device 6 stores various programs executed by the arithmetic processing device 4 and various data necessary for processing of the present system, and inputs / outputs such information to / from the arithmetic processing device 4. For example, the aviation laser measurement data 30 and the ortho image data 32 are stored in the storage device 6 in advance.

  The aviation laser measurement data 30 is acquired using, for example, a laser measurement system mounted on an aircraft, a helicopter, or the like. The laser measurement system includes a laser scanner and a GPS / IMU (Global Positioning System / Inertial Measurement Unit). The laser scanner sweeps a laser pulse from the sky toward the ground and receives the reflected pulse. The laser scanner used for acquiring the aviation laser measurement data 30 irradiates a near-infrared laser pulse and uses a laser scanner capable of recording a predetermined number (for example, many devices having four points) of reflected pulses for one irradiation pulse. The laser scanner gives the laser pulse irradiation direction and the time difference from pulse emission to reception, while the GPS / IMU gives the aircraft position and orientation, and the coordinates of the laser pulse reflection point are calculated from these data. The The aviation laser measurement data 30 includes, for example, the reflection intensity of the laser pulse and the coordinates of each reflection point for each laser pulse. Further, a laser measurement system compatible with full-wave measurement may be used.

  The ortho image data 32 is generated based on an aerial photographed image taken from an aircraft or the like. In this system, the ortho image data 32 is high-resolution image data such as aerial photographs taken from above the target area including the forest, and includes, for example, three components of red (R), green (G), and blue (B). Or a four-band multispectral image composed of four components obtained by adding near-infrared (NIR) to these three-band multispectral images.

  The input device 8 is a keyboard, a mouse, or the like, and is used for a user to operate the system.

  The output device 10 is a display, a printer, or the like, and is used to display the forest image generated by the present system to the user by screen display, printing, or the like. Further, the forest phase image may be output as data so that it can be used in other systems.

  The laser measurement feature quantity extraction unit 20 is configured to analyze one or more types of predetermined feature quantities (laser measurement feature quantities) based on the aviation laser measurement data 30 stored in the storage device 6 (targets). (Region) is calculated for each measurement point, interpolation is performed between the measurement points, a feature amount at each pixel is defined, and a feature amount image having the feature amount as a pixel value for the target region is generated. For example, the laser measurement feature amount extraction unit 20 is provided with an image generation unit for each type of feature amount for generating a feature amount image. In the present embodiment, an example in which the laser measurement feature amount extraction unit 20 includes a DCHM image generation unit 50, an RI image generation unit 52, a BRI image generation unit 54, and a PR image generation unit 56 is shown. Each of these image generation units will be described later.

  The aerial captured image feature amount extraction unit 22 calculates one or more predetermined feature amounts (aerial captured image feature amounts) for each measurement point in the target area based on the ortho image data 32 stored in the storage device 6. A feature amount image is generated with the feature amount as a pixel value for the target area. For example, the aerial captured image feature amount extraction unit 22 calculates one or both of predetermined texture information and predetermined spectrum information for each pixel of the ortho image data 32, for example. In the present system, the aerial captured image feature amount extraction unit 22 includes a texture analysis unit 60 that obtains texture information and a spectrum analysis unit 62 that obtains spectrum information. Each of these analysis units will be described later.

  The color image generation unit 24 generates a forest phase image in which the forest phase is visualized based on the feature amounts extracted by the laser measurement feature amount extraction unit 20 and the aerial captured image feature amount extraction unit 22. At this time, a color table may be prepared according to the feature amount, a predetermined pixel value is assigned to a feature amount value in a predetermined range, and a forest phase image may be generated.

  FIG. 2 is a schematic flow diagram of processing and data in the forest fauna analysis system 2. The laser measurement feature quantity extraction unit 20 extracts feature quantities from the aerial laser measurement data 30, and the aerial captured image feature quantity extraction unit 22 extracts feature quantities from the ortho image data 32, and each feature quantity image ( Feature image data 40) is generated. The color image generation unit 24 receives the feature amount image data 40 and generates a forest phase image (forest phase image data 42) in which the forest phase is visualized based on the feature amount.

  As the feature amount, for example, an amount that causes a difference in a value or a distribution range of the value according to the forest type is used. FIG. 3 is a schematic diagram for explaining an example of the feature amount extracted from the aviation laser measurement data 30, and shows an example in which a certain target area including a forest is observed in autumn. The figure shows numerical values obtained by observation for each of a plurality of feature amounts. For example, a numerical value corresponding to “D” is a feature amount value in a broadleaf forest, and similarly, “H”, The numerical values of “S” and “NF” are values in cypress forest, cedar forest, and non-forest areas.

  The digital canopy height model (DCHM) is a DSM that is normalized by removing the influence of the digital elevation model (Digital Terrain Model: DTM) included in the digital surface model (Digital Surface Model: DSM). Incidentally, DSM and DTM are generated from the aviation laser measurement data 30, and DTM is subtracted from DSM to generate DCHM. A place where the DCHM is lower than a certain threshold assumed from the forest (for example, about 15 cm in the example of FIG. 3) can be a non-forest area (NF) such as a water area, grassland, and clear land. Also, in the building area, the height obtained from the DCHM is greater than or equal to the threshold value assumed for the building, and the dispersion of the height may be smaller than that of the forest. Thus, the DCHM can distinguish between forest areas and building areas that are non-forest areas.

  In a forest area, a DSM is generated based on a first pulse among reflected laser pulses, a DTM is generated based on a last pulse, and a DTM is subtracted from the DSM to generate a DCHM. DCHM is used as data representing tree height. Although not shown in FIG. 3, the tree height reflects forest fauna information such as the growth status and age of the forest.

  The reflection intensity (RI) of the laser pulse reflects the reflection cross-sectional area and absorption rate of the tree. In particular, the difference in reflectance with respect to light having a wavelength in the near infrared region has been conventionally used for vegetation remote sensing. In the observation result shown in FIG. 3, the cypress forest (H) and the broad-leaved forest (D) show higher values than the cedar forest (S). Incidentally, the reflection intensity is defined based on the intensity of the first pulse. If the intensity of the irradiation pulse of the laser scanner used for measurement is constant, the reflection intensity can be expressed by the absolute value of the intensity of the reflection pulse. On the other hand, if the intensity of the irradiation pulse can change, the reflection intensity Is preferably expressed as a relative value normalized by the intensity of the irradiation pulse.

  FIG. 3 shows a total pulse (TP), a first pulse ratio (FPR), and an intermediate pulse ratio (IPR) as examples of the reflected pulse index. Here, values determined according to the number of reflected pulses with respect to the laser irradiation pulse are collectively referred to as a reflected pulse index. The index value is defined at each measurement point. The reflected pulse index based on the aviation laser measurement data acquired by the laser scanner with multi-pulse function can reflect the structural information inside the forest.

  TP is an index based on the total number of reflected pulses for each irradiation pulse, and can be, for example, the number of reflected pulses per unit area of a measurement point on the ground surface. As described above, the TP acquired by the laser scanner having the multi-pulse function basically does not necessarily match the number of irradiation pulses to the ground surface of the unit area.

  As shown in FIG. 3, it was observed that the TP in the cypress forest (H), cedar forest (S), and broadleaf forest (D) decreases in the order of broadleaf forest, cedar forest, and cypress forest.

  FPR is a value of the ratio of the number of first pulses to the total number of reflected pulses (that is, TP) from, for example, a unit area of a measurement point. Incidentally, the first pulse is a reflected pulse detected first with respect to the irradiation pulse. For example, when two laser pulses are irradiated to a unit area, two reflected pulses are received for one shot, and three reflected pulses are received for the other shot, TP is 5, Since the number of first pulses is 2, FPR is 2/5, that is, 40%.

  IPR is a value of the ratio of the number of intermediate pulses to the total number of reflected pulses (ie, TP) from, for example, a unit area of the measurement point. Incidentally, the intermediate pulse is a remaining pulse excluding the first detected first pulse and the last detected last pulse among the reflected pulses with respect to the irradiation pulse, and the number of reflected pulses for one irradiation pulse is 1 or 2. The number of intermediate pulses is zero. For example, when two laser pulses are irradiated to a unit area, two reflected pulses are received for one shot, and three reflected pulses are received for the other shot, TP is 5, Since the number of intermediate pulses is 1, the IPR is 1/5, that is, 20%.

  In the observation results of FIG. 3, FPR basically shows a tendency to be inversely proportional to TP. IPR basically has a positive correlation with TP, but the degree of difference between tree species differs between TP and IPR. Note that FPR and IPR are dimensionless quantities and are not easily affected by the irradiation density of laser pulses.

  Returning to FIG. 2, the processing performed by the laser measurement feature amount extraction unit 20 will be described. As described above, the reflection pulse index, the reflection intensity, and the tree height can be used as feature amounts. In the present embodiment, FPR (or IPR) is used as a feature amount for the reflected pulse index. In response to this, the DCHM image generation unit 50, the RI image generation unit 52, and the PR image generation unit 56 of the laser measurement feature amount extraction unit 20 generate the feature amount image data 40, respectively.

  Specifically, the DCHM image generation unit 50 generates DCHM from the aviation laser measurement data 30 and generates a DCHM image. The RI image generation unit 52 acquires the reflection intensity from the aviation laser measurement data 30 and generates an RI image. The PR (Pulse Ratio) image generation unit 56 calculates FPR or IPR as the pulse ratio PR from the aviation laser measurement data 30, and generates an FPR image or IPR image.

  Further, texture information obtained from a BRI (Binary Reflection Intensity) image generated by binarizing the RI image can also be used as a feature amount, and the BRI image generating unit 54 uses the BRI image as the feature amount image data 40. Generate. Here, the threshold value for binarization can be determined by, for example, the method of Otsu.

  FIG. 4 is an example of an aerial photograph (ortho image) of the target area. 5 to 9 are examples of feature amount images based on the aviation laser measurement data 30 acquired in the target area shown in FIG. 4, FIG. 5 is a DCHM image, FIG. 6 is an RI image, FIG. 7 is a BRI image, 8 is an FPR image, and FIG. 9 is an IPR image.

  As described above, the feature amount image data 40 is generated by the laser measurement feature amount extraction unit 20 and also by the aerial photographed image feature amount extraction unit 22. As the feature amount obtained from the ortho image data 32, at least one of texture information and spectrum information can be used. The texture information and the spectrum information are generated by the texture analysis unit 60 and the spectrum analysis unit 62 of the aerial captured image feature amount extraction unit 22, and either the texture information generated by the texture analysis unit 60 or the spectrum information generated by the spectrum analysis unit 62. Or all of them can be used as feature quantities.

  The texture analysis unit 60 generates an image BI, an image BGI, and an LBP image that are texture information from the ortho image data 32 as the feature amount image data 40.

  The image BI is a binary image obtained by binarizing the ortho image data 32. FIG. 10 is an explanatory diagram showing an example of the image BI. FIGS. 10A to 10D are examples of a cedar forest, a cypress forest, a broad-leaved forest, and a non-forest, respectively. Of the two images arranged side by side, the left side is the ortho image data 32 and the right side is the image BI. In the image BI, the white area is an area where the luminance is higher than the threshold in the ortho image data 32, and the black area is an area less than the threshold. Incidentally, the threshold value for binarization here can be determined by the method of Otsu, for example. For example, sunny and shaded distribution patterns appear in the image BI.

  The image BGI is an image obtained by binarizing the luminance gradient in the ortho image data 32 (binary gradient image). FIG. 11 is an explanatory diagram showing an example of the image BGI. FIGS. 11A to 11D are examples of cedar forest, cypress forest, broadleaf forest, and non-forest, respectively, as in FIG. 10. Of the two images arranged on the left and right, the left side is the ortho image data 32 and the right side is the image. BGI. In the image BGI, the white area is an area where the luminance gradient is equal to or greater than the threshold value, and the black area is an area where the brightness gradient is less than the threshold value. Again, the threshold can be determined, for example, by Otsu's technique. For example, from the image BGI, the spatial frequency of switching between the sun and the shade can be read.

  The LBP image is an image obtained from the ortho image data 32 using a local binary pattern operator. FIG. 12 is an explanatory diagram showing an example of an LBP image. FIGS. 12A to 12D are examples of a cedar forest, a cypress forest, a broadleaf forest, and a non-forest, respectively, as in FIGS. The right side is an LBP image. The LBP image reflects the detailed pattern structure pattern of the original image and has a characteristic that it is not easily affected by the contrast of the image.

  The spectrum analysis unit 62 extracts spectrum information from the ortho image data 32. In the present embodiment, the spectrum analysis unit 62 performs normalization processing represented by the following expression on the 4-band multispectral image of R, G, B, and NIR, and normalizes the components R ′, G ′, and B ′. Generate an image consisting of

  Note that the spectrum analysis unit 62 may be configured to output a 4-band multispectral image of R, G, B, and NIR that is not subjected to normalization processing.

  As described above, the DCHM image generation unit 50, the RI image generation unit 52, the BRI image generation unit 54, the PR image generation unit 56, the texture analysis unit 60, and the spectrum analysis unit 62 generate the feature amount image data 40.

  The color image generation unit 24 uses the combination of the laser measurement feature amount and the aerial captured image feature amount obtained as the feature amount image in the feature amount image data 40, as a forest phase image (forest phase image data 42). Generate an image. The color image generation unit 24 uses a plurality of imaging feature amounts that are laser measurement feature amounts or aerial captured image feature amounts, respectively, and based on the value of the imaging feature amount in an area corresponding to each pixel, a pixel of the color image Determine the value. Specifically, the color image generation unit 24 determines the pixel value of each pixel of the forest phase image based on the pixel value of the pixel in the feature amount image corresponding to the imaging feature amount.

  The color image generation unit 24 determines a pixel value by converting a set of imaging feature values (feature value set) into, for example, R, G, and B coordinate values in the RGB color space. The conversion can be, for example, linear conversion. In this case, if a column vector composed of n types (n ≧ 2) of imaging features to be converted is μ and a column vector composed of RGB coordinate values is ν. The conversion is expressed by ν = Mμ using a matrix M of 3 rows and n columns, for example. When n is 3 or more, M is basically set so that the coordinate values of RGB are linearly independent, thereby obtaining a color image composed of three colors. On the other hand, when n is 2, RGB is linearly dependent, but a color image composed of two colors can be generated. In addition, each RGB coordinate value may be a linear sum of a plurality of imaging feature values or a single imaging feature value.

  At least one of the plurality of imaging feature quantities constituting one forest phase image is a laser measurement feature quantity, and at least one is an aerial captured image feature quantity. An example of a set of imaging feature values for obtaining a suitable forest image includes a tree height. In this case, the DCHM image generation unit 50 generates a DCHM image as a feature amount image, and the color image generation unit 24 generates a forest phase image using the DCHM image. By using a DCHM image (tree height) as an imaging feature amount, an image capable of discriminating forest fauna having different tree ages and growth conditions even with the same tree species can be obtained.

  Here, the RGB value of each pixel of the forest phase image is represented as (R, G, B). Below, the definition formulas of RGB values in two examples of forest phase images generated using DCHM images are shown. In the following equations, “DCHM”, “FPR”, and “BRI” represent pixel values of the DCHM image, FPR image, and BRI image, respectively, and “G ′” and the like represent G components after normalization processing. .

  (DCHM, G ', FPR)

  (0.5R '+ 0.5DCM, 0.5G' + 0.5FPR, 0.5B '+ 0.5BRI)

  Such a definition formula is set in the storage device 6 or the color image generation unit 24 in advance, and the color image generation unit 24 converts the imaging feature amount into the RGB value of the forest phase image based on the definition formula. The conversion from the imaging feature quantity to the color is not limited to the above example. For example, the component of the column vector μ described above is an arbitrary laser measurement feature quantity generated by the laser measurement feature quantity extraction unit 20 and an aerial captured image. A combination with any aerial captured image feature amount generated by the feature amount extraction unit 22 can be used. Further, for example, in the second example shown above, instead of the BRI image that is the texture information extracted from the aviation laser measurement data 30, the image BI, the image BGI, which are extracted from the ortho image data 32 by the texture analysis unit 60, LBP images can be used. Further, the conversion is not limited to linear conversion. Further, the RGB components may be interchanged.

The color system is not limited to the RGB color system. For example, in the L ^* a ^* b ^* color system, DCM is associated with lightness L, and differences in tree height and forest / non-forest distinction can be represented by lightness. For example, as the pixel value of the DCHM image is larger, that is, the tree height is higher, the L ^* component is set larger and displayed brighter. In this case, the non-forest with a small value of DCHM is displayed darkly.

Another color system is the HSV color system. In the color system, a color space including three components of hue (Hue), saturation (Saturation, Chroma), and lightness (Value, Lightness) is defined. Similar to the HSV color system, there is an HLS (or HSL, HSI) color system, in which there are three colors: Hue, Saturation, and Luminance (Lightness, Luminance, Intensity). A color space consisting of components is defined. In these color systems, the hue is expressed by an angle (0 to 360 °) along the color wheel, that is, a one-dimensional amount. Here, as understood from FIG. 3, there is a difference between the three types of tree species (H, S, D) in the reflected pulse index (TP, FPR, IPR), and the tree type is discriminated only by the reflected pulse index. Is possible. Therefore, for example, a hue component such as an HSV color system can be determined based on the FPR, and a color image can be generated. Further, in the HSV color system, etc., the brightness (luminance) is associated with DCHM, and as described in the L ^* a ^* b ^* color system, the difference in tree height and the distinction between forest / non-forest is represented in the color image. be able to. In the HSV color system or the like, texture information extracted from the aviation laser measurement data 30 and the ortho image data 32 can be associated with saturation.

  Since a relational expression in a certain color system can be converted into a relational expression in other various color systems, the color image generation unit 24 generates forest image data 42 expressed in a desired color system. be able to.

  The forest phase analysis system 2 may be configured to generate a plurality of forest phase images with different combinations of imaging feature amounts by the color image generation unit 24 and display the plurality of color images on the output device 10 serving as a display unit so as to be comparable. Good. For example, the arithmetic processing device 4 displays a plurality of types of color images in the same target area side by side on the display screen, or switches the screens according to user instructions input using the input device. To do.

  A plurality of forest phase images are respectively generated based on a plurality of imaging feature amounts, and these imaging feature amounts include both a laser measurement feature amount and an aerial captured image feature amount. Note that some of the plurality of forest phase images may be generated based only on the laser measurement feature amount, or may be generated based only on the aerial captured image feature amount.

  As described above, the forest phase analysis system 2 according to the present invention generates a forest phase image by using aerial images such as aerial photographs and laser measurement data in combination.

  For example, from aerial photography, color information in a wide wavelength band can be obtained with high resolution in the target area, but the color is affected by the shooting season, and different tree species may be very similar in color. . Laser measurement data, on the other hand, provides information on forest topography, tree height, crown surface, and internal structure, but not as high-resolution information as aerial photography, and information in the near-infrared wavelength region. Can only be obtained. The forest phase analysis system 2 can generate a color image that makes it easy to discriminate the forest phase difference by using the aerial captured image and the aerial laser measurement data in combination.

  For example, by using the height information obtained from the laser measurement data, it is possible to distinguish forest types having different tree ages and growing conditions even in the same tree species. On the other hand, high-resolution texture information is generated from aerial photographs, and is reflected in the difference in forest fauna on color images. In addition, by using laser measurement data together, the effects of aerial photography on the forest phase image are relatively reduced due to the timing of shooting, shooting direction and angle, and the azimuth and altitude angle of the sun. It is possible to create and present to the user a forest phase image in which differences in forest phases appear stably and accurately. The ortho image data 32 may be acquired based on a satellite image.

  2 Forest Phase Analysis System, 4 Arithmetic Processing Device, 6 Storage Device, 8 Input Device, 10 Output Device, 20 Laser Measurement Feature Extraction Unit, 22 Aerial Image Feature Extraction Unit, 24 Color Image Generation Unit, 30 Aircraft Laser Measurement Data , 32 Ortho image data, 40 Feature amount image data, 42 Forest phase image data, 50 DCHM image generation unit, 52 RI image generation unit, 54 BRI image generation unit, 56 PR image generation unit, 60 Texture analysis unit, 62 Spectrum analysis unit .

Claims (6)

Laser measurement feature quantity extraction means for extracting one or more predetermined laser measurement feature quantities based on aerial laser measurement data obtained by scanning a target area including a forest;
An aerial photographed image feature amount extracting means for extracting one or more predetermined aerial photographed image feature amounts based on an aerial photographed image obtained by photographing the target area from above;
A means for generating a color image of the target area based on a set of the laser measurement feature quantity and the aerial photographed image feature quantity, each of which is the laser measurement feature quantity or the aerial photographed image feature quantity. Color image generation means for determining a pixel value of the color image based on a value in a region corresponding to the pixel of the normalized feature amount;
A forest phase analysis apparatus characterized by comprising: In the forest phase analysis apparatus according to claim 1,
The laser measurement feature amount extraction unit uses the reflected pulse index at each measurement point, the reflection intensity of the laser, and the reflection intensity of the laser as the laser measurement feature amount according to the number of reflection pulses with respect to the laser irradiation pulse. Extracting at least one of a texture feature amount extracted from a pattern obtained by binarizing an image in the target area as a value, and a tree height;
The aerial captured image feature amount extraction means extracts at least one of texture information and spectrum information extracted from the aerial captured image as the aerial captured image feature amount;
Forest phase analysis device characterized by In the forest fauna analysis apparatus according to claim 1 or 2,
The laser measurement feature amount extraction means extracts at least a tree height as the laser measurement feature amount,
The color image generating means generates the color image including the tree height in the imaging feature amount;
Forest phase analysis device characterized by In the forest phase analysis apparatus according to claim 3,
A display means for displaying the plurality of color images in a comparable manner;
The color image generation means generates the plurality of color images to be displayed by the display means with different combinations of the plurality of imaging feature amounts;
Forest phase analysis device characterized by A laser measurement feature extraction step for extracting one or more predetermined laser measurement features based on aerial laser measurement data acquired by scanning a target area including a forest;
An aerial captured image feature amount extracting step for extracting one or more predetermined aerial captured image feature amounts based on an aerial captured image captured from above the target area;
A processing step of generating a color image of the target area based on a set of the laser measurement feature quantity and the aerial photographed image feature quantity, each of which is a plurality of laser measurement feature quantities or aerial photographed image feature quantities A color image generating step for determining a pixel value of the color image based on a value in a region corresponding to a pixel of the imaging feature amount;
The forest phase analysis method characterized by having. A program for causing a computer to perform forest analysis,
Laser measurement feature amount extraction means for extracting one or more predetermined laser measurement feature amounts based on aerial laser measurement data acquired by scanning a target area including a forest;
An aerial photographed image feature quantity extracting means for extracting one or more predetermined aerial photographed image feature quantities based on an aerial photographed image obtained by photographing the target area from above; and
A means for generating a color image of the target area based on a set of the laser measurement feature quantity and the aerial photographed image feature quantity, each of which is the laser measurement feature quantity or the aerial photographed image feature quantity. A color image generating means for determining a pixel value of the color image based on a value in a region corresponding to the pixel of the normalized feature amount;
A program characterized by functioning as

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