JP2015023857A - Forest phase analyzer, forest phase analysis method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make it possible to automatically generate a forest phase segment map from an aerial photograph etc. with high accuracy.SOLUTION: A forest phase analyzer comprises: a statistic distribution creation unit 26; a teacher data determination unit 28; and a forest phase segment map creation unit 30. The statistic distribution creation unit 26 sets an attention region in an original image which is obtained by photographing a target region from the sky at a second period corresponding to each forest phase region of the target region including the forest in an existing forest phase segment map at a first period so that statistic distribution of characteristic information with respect to the image extracted by a prescribed element region in an attention region of each forest phase is obtained. The teacher data determination unit 28 determines, when non-change region where the forest phase is not changed from the first period in each attention region occupies the greater part of the attention region, the teacher data with respect to the characteristic information of the forest phase corresponding to the non-change region is determined based on the statistic distribution of the attention region. The forest phase segment map creation unit 30 determines the forest phase in each element region of the original image on the basis of the determined teacher data of each forest phase to create the forest phase segment map for the second period.

Description

  The present invention relates to a forest facies analysis apparatus, a forest facies analysis method, and a program for generating a forest facies segment map corresponding to an image taken from the sky from the image and an existing forest facies segment map at a different time from the image.

  Remote sensing from the sky by aircraft or the like can grasp the situation on the ground over a wide range, and is used to detect forest changes due to felling, wind damage, etc., and to create a forest fauna classification map. Incidentally, in Japan, forest planning and management is generally conducted in units of several years. Corresponding to this, the forest phase classification map is also updated every several years.

  Conventionally, human detection of forest changes and updates of forest phase classification maps using existing forest phase classification maps and newly acquired high-resolution image data such as aerial photographs are basically carried out by visual interpretation. However, since a large amount of time is required for visual interpretation over a wide area, more efficient methods for detecting forest changes and updating the forest fauna classification map are required.

  As a conventional technique for automatically interpreting forest changes, there is known a method for comparing a forest phase map created from an image of a new year with an existing forest phase map. In addition, regarding the technology for automatically generating a forest fauna map from an image of forests, the image is divided into regions to generate forest fauna compartments that are a plurality of small regions with similar characteristics, and forest fauna compartments for each forest fauna compartment. Methods for classification are being studied.

JP 2008-046837 A JP 2011-024471 A

  In the automatic generation of the forest fauna classification map, the forest fauna classification is performed using the teacher data acquired from the image of the forest. Conventionally, when acquiring teacher data, a human selects a region corresponding to each forest phase, and the teacher data of each forest phase is extracted from the region. For this reason, the preparation of the forest facies map was not fully automated, and the updating of the forest facies map was not sufficiently efficient and automated.

  In addition, the acquisition of the teacher data regarding the above-mentioned forestry is performed from a part of the image due to the human workload. Here, even in the same forest phase, the characteristics vary within the image. For this reason, there is a problem that misclassification is likely to occur in the forest fauna classification map generated using the teacher data obtained from a part of the image. In addition, there is a problem that correction of the misclassification takes a lot of work.

  The present invention has been made to solve the above problems, and automatically generates a forest fauna map corresponding to a high-resolution image such as aerial photographs with high accuracy using an existing forest fauna map. It is an object of the present invention to provide a forest facies analysis apparatus, forest facies analysis method and program that enable automatic detection of forest changes.

  (1) The forest fauna analysis apparatus according to the present invention corresponds to each forest fauna area in the known forest fauna division map in the first period of the target area including the forest, and the target area is viewed from the sky in the second period. A statistical distribution creating means for setting a region of interest in the captured original image and obtaining a statistical distribution for image feature information extracted for each predetermined element region in the region of interest of each forest phase; The feature information of the forest facies corresponding to the unchanged area based on the statistical distribution in the attention area under the condition that most of the unchanged area where the forest phase does not change between the first period is occupied Teacher data determining means for determining teacher data and the teacher data determined for each forest phase determine the forest phase in each element region of the original image, and the forest phase in the second period It has a forest type classification diagram generating means for generating a partial figure, a.

  (2) In the forest fauna analysis apparatus described in (1) above, the forest fauna in the target area is further compared by comparing the forest fauna segment map in the first period and the forest fauna segment map in the second period. It can also be set as the structure which has the forest phase change detection means which detects the change of this.

  (3) In the forest fauna analysis apparatus described in the above (1) or (2), the target area is further divided into a plurality of regions based on the similarity of one or more types of partition feature quantities of the original image. A forest fauna compartment generating means for generating and outputting the forest fauna compartment may be provided, and the element region in the statistical distribution creating means and the forest fauna compartment map generating means may be the forest fauna compartment.

  (4) In the forest fauna analysis apparatus described in (3) above, image degradation means for generating a degraded image in which the resolution is reduced from the original image using a region composed of a plurality of adjacent pixels of the original image as a pixel. An initial compartment means for dividing the degraded image into a plurality of regions based on the similarity of the one or a plurality of types of feature values, and creating a forest fauna compartment; and an adjacent forest fauna compartment Are combined on the basis of the similarity of the one or a plurality of types of the feature values for the partition to generate a new forest fauna partition once or a plurality of times, and the forest fauna partition of the forest in the target area is obtained. And subsequent partition means for outputting, the succeeding partition means performs at least one sequential partition processing on the partition feature amount of the original image, or each of the sections of the original image and the degraded image. It can be configured to perform, based on the use feature amount.

  (5) In the forest fauna analyzing apparatus described in (3) or (4), the forest fauna analyzing apparatus includes texture information extracting means for extracting texture information from the original image, and the statistical distribution creating means is output from the forest fauna section generating means. The statistical distribution can be obtained by using the texture information extracted from the forest stand sections as the feature information.

  (6) The forest fauna analysis apparatus according to (5), further including spectrum information extraction means for extracting spectrum information from the original image, wherein the statistical distribution creation means is the forest fauna output from the forest fauna section generation means. The statistical distribution can be obtained by using the texture information and the spectrum information extracted from the section as the feature information.

  (7) In the forest fauna analysis apparatus according to (3) or (4), the forest fauna analyzing apparatus includes a spectrum information extracting unit that extracts spectrum information from the original image, and the statistical distribution creating unit outputs from the forest fauna section generating unit. The statistical distribution can be obtained using the spectrum information extracted from each forest fauna section as the feature information.

  (8) In the forest fauna analysis method according to the present invention, the target area is photographed from the sky in the second period corresponding to the area of each forest fauna in the forest fauna division map in the first period of the target area including the forest. A statistical distribution creating step of setting a region of interest in an original image, and obtaining a statistical distribution for image feature information extracted for each predetermined element region in the region of interest of each forest phase; Teacher data regarding the feature information of the forest facies corresponding to the unchanged area based on the statistical distribution in the area of interest under the condition that most of the unchanged area where the forest fauna does not change between And determining the forest fauna in each element area of the original image by the teacher data determining step for determining each forest phase and the forest data determined for each forest phase. Has a forest type classification diagram generating step of generating a classification diagram, the.

  (9) A program according to the present invention is a program for causing a computer to perform a forest facies analysis in a target area including a forest, and the computer is a forest facies division diagram in a first period of the target area including the forest. Corresponding to each forest phase area, a target area is set in the original image of the target area taken from above in the second period, and extracted for each predetermined element area in the target area of each forest phase. Statistical distribution creating means for obtaining a statistical distribution for the feature information of the image, under the condition that most of the unchanged areas in which the forest fauna does not change between the first period in each of the attention areas, Teacher data determination means for determining teacher data related to the feature information of the forest fauna corresponding to the unchanged area based on the statistical distribution, and determination for each forest fauna Wherein said original image by the teacher data to determine forest type in each element region, said second forest type classification diagram generating means for generating a forest type classification diagram in time, to function as a.

  According to the present invention, it is possible to automatically generate a forest phase map corresponding to a high-resolution image such as an aerial photograph using an existing forest phase map with high accuracy, and automatic update of the forest phase map. , And automatic detection of forest changes.

It is a block diagram which shows the structure of the outline of the forest facies analysis system which is the 1st Embodiment of this invention. It is explanatory drawing which shows the example of a sunshine / shade distribution pattern. It is explanatory drawing which shows the example of a sunny / shade boundary pattern. It is explanatory drawing which shows the example of a LBP image. It is an image which shows the example of the ortho image before a normalization process. 6 is an image obtained by normalizing the image of FIG. It is a schematic data flow figure in the forest facies analysis system which is an embodiment of the present invention. It is a general | schematic flowchart explaining the production | generation process of the forest fauna compartment by a forest fauna compartment production | generation part. It is an image which shows the example of the forest fauna division produced | generated from the low-resolution image in the first hierarchy of a hierarchical area | region division process. It is an image which shows the example of the forest fauna division produced | generated in the middle hierarchy of the hierarchical area | region division process. It is an image which shows the example of the forest fauna division produced | generated in the hierarchy after the hierarchy shown in FIG. It is a general | schematic flowchart explaining the extraction process of the feature information data by a texture analysis part and a spectrum analysis part. It is a schematic diagram of the statistical distribution regarding an attention area. It is a block diagram which shows the structure of the outline of the forest facies analysis system which is the 2nd Embodiment of this invention.

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

[First Embodiment]
FIG. 1 is a block diagram showing a schematic configuration of a forest facies analysis system 2 according to the first 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 is composed of a CPU (Central Processing Unit) of a computer, and will be described later in a forest phase section generation unit 20, a texture analysis unit 22, a spectrum analysis unit 24, a statistical distribution creation unit 26, a teacher data determination unit 28, and a forest phase division diagram. It functions as the generation unit 30 and the forest phase change detection unit 32.

  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 storage device 6 stores in advance ortho image data 40, forest facies section map data 42, spectral conditions 44 and geometric conditions 46 relating to forest facies sections.

  The ortho image data 40 is generated based on an aerial photograph image taken from an aircraft or the like. In this system, the ortho image data 40 is high-resolution image data used as an original image obtained by photographing a target area including a forest from the sky. For example, three components of red (R), green (G), and blue (B) are used. Or a four-band multispectral image composed of four components obtained by adding the near infrared (NIR) to the three-band multispectral image.

  The forest phase map data 42 is data representing an existing forest phase map for the target area shown in the ortho image data 40. The forest fauna division map data 42 represents forest fauna sections that are elemental areas obtained by dividing the target area into regions and forest faunas in the sections. Here, it is assumed that the forest phase classification map data 42 relates to the time T1, while the ortho image data 40 relates to the time T2 different from the time T1. Although the anteroposterior relationship between T1 and T2 is arbitrary, in the example shown in the present embodiment, T1 is assumed to precede T2.

  The spectral condition 44 and the geometric condition 46 are conditions to be satisfied by the forest fauna section generated by dividing the original image into regions. The spectrum condition 44 defines a condition relating to a spectral feature amount extracted from the color, density, and brightness of the pixel group constituting the forest fauna compartment. For example, a statistical quantity such as an average value or standard deviation of each color (band) can be used as the spectral feature quantity. On the other hand, the geometric condition 46 defines a condition related to a shape (Shape) which is a geometric feature amount of the forest fauna section. For example, the shape is represented by parameters such as compactness and smoothness.

  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 a forest stand section or a forest stand division map (forest stand map) generated by the present system to a user by screen display, printing, or the like. Moreover, you may output as data so that the data regarding forest facies division and forest facies classification can be used in other systems.

  The forest phase section generation unit 20 performs a region division process on the ortho image data 40 in a hierarchical manner to generate forest phase sections. The forest phase section generation unit 20 includes a low resolution image generation unit 70, an initial partition section 72, and a subsequent partition section 74.

  The low-resolution image generation unit 70 is an image deterioration unit that generates a low-resolution image (degraded image) in which the resolution is lowered by performing a gradation process on the high-resolution ortho-image data 40, and the pixels of the low-resolution image are ortho-images. This corresponds to a region composed of a plurality of adjacent pixels in the data 40. Here, for easy discrimination, the pixels of the ortho image data 40 are expressed as original pixels, and the pixels of the low resolution image are expressed as enlarged pixels. For example, the low-resolution image can be generated by down-sampling the ortho-image data 40 at a uniform sampling density, and the pixel value of the enlarged pixel is representative of a plurality of original pixels in the corresponding region in the ortho-image data 40. Can be set to a value. For example, the low-resolution image generation unit 70 performs smoothing processing on the original image by convolution using a predetermined filter, and the pixel value of the enlarged pixel is subjected to smoothing processing at the original pixel at a predetermined position in the enlarged pixel. Can be set to the pixel value. In a general image, the luminance value of a pixel that is close to the target pixel often becomes closer to the luminance value of the target pixel, and the difference from the luminance value of the target pixel often increases as the distance from the target pixel increases. Considering these properties, a Gaussian filter that smoothes an image using a two-dimensional Gaussian distribution is known so that the weight when calculating an average value increases as it is closer to the target pixel, and the weight decreases as it is further away. It has been. This Gaussian filter can be employed for the smoothing process when obtaining the pixel value of the enlarged pixel. As a simpler method, for example, the pixel value of the enlarged pixel can be defined by an average value, a median value, or the like of the pixel values of a plurality of original pixels corresponding thereto.

  The initial partition unit 72 performs processing of the first hierarchy of hierarchical area division processing. Specifically, one or both of the spectral feature quantity and the geometric feature quantity of the image are set as the feature values for partitioning, and the initial partition unit 72 uses the low resolution image generated by the low resolution image generation unit 70 as the feature value for partitioning. Based on the similarity, it is divided into a plurality of regions, and primary forest fauna sections are generated. For example, the initial partition unit 72 determines whether or not a region composed of a plurality of adjacent enlarged pixels is a primary forest phase compartment, similarity of spectral feature amounts (spectral conditions) of pixel values of these enlarged pixels, and It is determined based on the similarity (geometric condition) of the geometric feature amount for the region obtained by combining these enlarged pixels.

  The succeeding partition unit 74 performs processing after the second layer of the hierarchical area division processing. Specifically, the succeeding partition unit 74 performs a sequential partition process once or a plurality of times by combining adjacent forest fauna partitions to generate a new forest fauna partition. Sequential parcel processing is based on the similarity of their spectral features for a plurality of adjacent low-order forest fauna compartments and the similarity of geometric features for higher-order forest fauna compartments obtained by combining them. Decide whether or not to join these lower forest fauna compartments.

  In the present embodiment, the subsequent partitioning unit 74 performs the sequential partitioning process between the low resolution image and the high resolution image (original image) in the forest phase partitioning that are the result of the region division processing of the low resolution image generated by the initial partitioning unit 72. Region segmentation processing is performed using both or only high-resolution images. From the low-resolution image, the spectral feature amount for the enlarged pixel group included in the forest phase section is obtained as the spectral feature amount of the pixel value used for determining whether or not the forest phase section can be combined. On the other hand, from the high-resolution image, a spectral feature amount for the original pixel group included in the forest phase section is obtained as a spectral feature amount of the pixel value used for determining whether or not the forest phase section can be combined. The subsequent partitioning unit 74 performs at least one sequential partitioning process based on the spectral feature amount of the pixel value of the high resolution image or the spectral feature amount of the pixel value of each of the high resolution image and the low resolution image. It can also be set as the structure which is. That is, when the subsequent partitioning unit 74 performs a plurality of sequential partitioning processes, a part of the number of times may extract a spectral feature amount from only the pixel values of the low resolution image, and perform the region partitioning process based thereon. .

  For example, a technique based on region merging can be used for image region segmentation. In this method, whether or not two adjacent objects existing in an image to be subjected to image region division processing are integrated depends on the heterogeneity of a new object generated after integration and the heterogeneity of an object before integration. Is determined by evaluating the change between. Incidentally, the object is a pixel of a low-resolution image in the process in the initial partition unit 72, and the object is a forest phase partition that has already been generated in the process in the subsequent partition unit 74.

Specifically, the object heterogeneity change Δh due to region merging is calculated from the spectral heterogeneity change Δh _p and the shape heterogeneity change Δh _t before and after merging by the following equation.

Here, w _p is a spectral heterogeneity weight, and w _t is a shape heterogeneity weight.

The change in spectral heterogeneity Δh _p before and after merging is calculated by the following equation using the standard deviation of the pixel values in the object before and after merging in each band of the target image.

Here, N is the number of bands of the image, w _i is the weight of band i, n _ab is the number of pixels of the new object after merging, n _a and n _b are the number of pixels of the two objects before merging, σ _{i, ab} Is the standard deviation in band i of the object after merging, and σ _{i, a} , σ _{i, b} is the standard deviation in band i of the two objects before merging.

The change Delta] h _t in shape heterogeneity around merging is defined by the following equation in two criteria of compactness and smooth ness.

Here, Δh _c is a change in compactness before and after merging, Δh _s is a change in smoothness before and after merging, w _c is a weight for compactness, and w _s is a weight for smoothness.

The object compactness criterion is calculated from the object perimeter and area, while the smoothness criterion is calculated from the object perimeter and the bounding box diameter (major axis). Specifically, Δh _c and Δh _s are defined by the following equations.

Here, l _ab is the perimeter of the object after merging, l _a and l _b are the perimeter of the two objects before merging, s _ab is the area of the object after merging, and s _a and s _b are the two before the merging. The area of one object, b _ab is the diameter of the bounding box of the object after merging, b _a , b _b are the diameters of the bounding box of the two objects before merging, n _ab is the number of pixels of the new object after merging, n _a , _Nb is the number of pixels of the two objects before merging.

  When the change Δh of the object heterogeneity before and after merging does not exceed the set threshold value, the region merging process is performed, and when it exceeds the threshold value, the region merging process is stopped. The set threshold value is called a scale parameter, and represents the size of an object generated by the image division process. The larger the scale parameter, the more objects are merged, and the size of the object that is finally generated by area division increases.

As shown in the equation (1), the contribution ratio of each of the spectral feature quantity and the geometric feature quantity to the judgment of the combination of the enlarged pixels in the initial partition section 72 or the judgment of the combination of the lower order forest fauna sections in the subsequent partition section 74 is a weight. It can be adjusted by w _p and w _c . Here, one or both of the initial partition unit 72 and the subsequent partition unit 74 may be configured to perform determination based on the similarity of the spectral feature amount without using the geometric condition in the determination of the forest phase partition generation.

  The texture analysis unit 22 is a texture information extraction unit that extracts texture information from the ortho image data 40. The texture analysis unit 22 includes a sunshine / shade distribution pattern information extraction unit 80, a sunshine / shade boundary pattern information extraction unit 82, and an LBP image information extraction unit 84.

  The sun / shade distribution pattern information extraction unit 80 binarizes the ortho image data 40 to extract the sun / shade distribution pattern in which the sun and the shade are distinguished as texture information. FIG. 2 is an explanatory diagram showing an example of the sunny / shade distribution pattern. FIGS. 2A to 2D are examples of a cedar forest, a cypress forest, a broadleaf forest, and a non-forest, respectively. Of the two images arranged on the left and right, the left side is the ortho image data 40, and the right side is the sunshine / shade distribution pattern. is there. In the sunshine / shade distribution pattern, the white area is sunshine and the black area is shade. Incidentally, the threshold for binarization here is determined by Otsu's method.

  The sun / shade boundary pattern information extraction unit 82 binarizes the luminance gradient in the ortho image data 40 and extracts the sun / shade boundary pattern in which the boundary region between the sun and the shade appears as texture information. FIG. 3 is an explanatory diagram showing an example of a sunny / shade boundary pattern. FIGS. 3A to 3D are examples of cedar forest, cypress forest, broadleaf forest, and non-forest, respectively, as in FIG. 2. Of the two images arranged side by side, the left side is ortho image data 40 and the right side is Hinata. / Shade boundary pattern. In the sun / shade boundary pattern, the white area is an area where the luminance gradient is greater than or equal to the threshold value, and the black area is an area where the brightness gradient is less than the threshold value. Again, the threshold is determined by Otsu's method. From the Hinata / Shade boundary pattern, the spatial frequency of switching between the Hinata and the Shade can be read.

  The LBP image information extraction unit 84 extracts an LBP (Local Binary Pattern) image obtained from the ortho image data 40 using a local binary pattern operator as texture information. FIG. 4 is an explanatory diagram showing an example of an LBP image. 4A to 4D are examples of a cedar forest, a cypress forest, a broad-leaved forest, and a non-forest, respectively, as in FIG. 2 and FIG. 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 texture analysis unit 22 calculates statistics such as an average value and a standard deviation as the feature values of the extracted sunny / shade distribution pattern, sunny / shade boundary pattern, and LBP image.

  The spectrum analysis unit 24 is a spectrum information extraction unit that extracts spectrum information from the ortho image data 40. In the present embodiment, the spectrum analysis unit 24 performs a normalization process 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 ′. A spectral feature amount is extracted from the image consisting of. Further, the spectrum analysis unit 24 extracts a spectrum feature amount only from the sunny area in each forest phase section based on the sunny / shade distribution pattern information.

  5 and 6 are images showing an example of normalization processing for the ortho image data 40. FIG. 5 shows an original image, that is, an ortho image before normalization processing, and FIG. 6 shows the ortho image data 40 corresponding to FIG. It is an image obtained by normalization processing. Although FIGS. 5 and 6 are displayed in gray scale, FIG. 5 is originally an RGB composite color image, and FIG. 6 is an R′G′B ′ composite color image. For example, due to the difference in the size of the near-infrared component according to the difference in the tree species or the like, a difference in hue or the like occurs between the original image and the normalized image. The difference between the original image and the normalized image in FIG. 5 and FIG. 6 can be read because the change in shading in the image is different between the two images.

  In addition, the spectrum analysis part 24 can also be set as the structure which extracts a spectrum feature-value without performing a normalization process, or the structure which extracts the spectrum feature-value in the whole each forest fauna division including a shade.

  The statistical distribution creation unit 26 corresponds to the region of each forest phase in the known forest phase map at time T1 given as the forest phase map data 42, and focuses on the original image at time T2 given in the ortho image data 40. And a statistical distribution is obtained for the feature information of the image extracted for each predetermined element region in the attention region of each forest phase.

  The feature information is texture information and spectrum information extracted from the original image by the texture analysis unit 22 and the spectrum analysis unit 24. In the present embodiment, corresponding to using texture information as feature information of an image for which a statistical distribution is obtained, for example, a forest fauna section generated for an original image is set as an element region. In addition, when only the spectrum information is used as the feature information of the image for obtaining the statistical distribution, the pixel can be used as the element region.

  For example, a region of interest related to a cedar forest is set in the original image corresponding to a set of forest facies sections of a cedar forest in the forest fauna division map at the time T1. The region of interest is the same or approximately the same region as a region composed of a set of cedar forest facies sections in the forest facies section diagram at time T1. Specifically, a set of forest phase sections generated for the original image that are present at a position corresponding to the cedar forest area at time T1 can be set as an attention area regarding the cedar forest. Here, when a certain forest fauna section of the original image straddles a plurality of forest fauna areas at the time T1, for example, the forest fauna section has the most overlap with the forest fauna section among the forest fading areas at the time T1. It can be included in the attention area of the forest fauna corresponding to the larger one. In addition, weighting can be performed according to the ratio of the overlapping area and reflected in the statistical distribution.

  As a statistical distribution, a frequency distribution is obtained using a feature amount representing texture information and spectrum information as image feature information as variables. There may be only one type of variable or a plurality of variables. The frequency may be simply the number of elemental areas, or in the case of using forest stand sections as element areas, values weighted according to the area of forest stand sections may be integrated.

  The teacher data determination unit 28 determines teacher data of each forest phase regarding the feature information in the original image at the time T2. Here, as already mentioned, the forest phase map is usually updated every few years. That is, the interval between the time T1 and the time T2 is basically about several years. The forest phase analysis system 2 efficiently performs a wide range of forest phase analysis using the ortho image data 40 taken by an aircraft or the like, and a large-scale forest phase change within a short period of about several years in the wide target area. There is not much that happens. That is, normally, it can be expected that the area where the forest fauna changes between time T1 and time T2 is a small part of the wide target area, and in each attention area set for the original image of time T2, It is possible to perform a statistical analysis on the assumption that most of the unchanged areas where the forest fauna does not change from the time T1 when the existing forest fauna classification map is obtained. Therefore, the teacher data determination unit 28 extracts the feature value of the image in the position of the maximum peak in the statistical distribution obtained for the attention region corresponding to a certain forest aspect or a predetermined vicinity thereof as the feature data of the forest structure at the time T2. In addition, it is used as teacher data when performing the forest phase classification of the original image.

  The statistical distribution in the attention area corresponding to a certain forest fauna basically reflects the feature information of all forest fauna sections having the forest fauna in the target area. Therefore, by using the feature information corresponding to the peak in the statistical distribution as the teacher data, highly reliable teacher data that is less affected by variations in the feature information of the forest fauna sections shared by the forest fauna can be obtained.

  The forest fauna map generation unit 30 has obtained the texture information extracted by the texture analysis unit 22 from each forest phase section of the original image and / or the spectrum information extracted by the spectrum analysis unit 24 by the teacher data determination unit 28. Classification based on the teacher data is performed to determine the forest fauna in each forest fauna section, and a forest fauna division map at time T2 is generated.

  The forest phase change detection unit 32 compares the existing forest phase map at the time T1 with the forest phase map at the time T2 generated by the forest phase map generation unit 30, and detects the forest phase change in the target area.

  FIG. 7 is a schematic data flow diagram in the forest facies analysis system 2. Hereinafter, the processing of the forest facies analysis system 2 will be described with reference to FIG.

  The forest phase section generation unit 20 generates forest phase section data 100 at the time T2 from the ortho image data 40 at the time T2. FIG. 8 is a schematic flow diagram illustrating the forest fauna compartment generation process by the forest fauna compartment generator 20. The forest phase section generation unit 20 generates a low resolution image from the ortho image data 40 by the low resolution image generation unit 70 (S10). Further, the forest facies section generation unit 20 hierarchically divides the forest fauna section obtained by dividing the low resolution image into regions, and generates a forest fauna section corresponding to a region composed of a uniform forest fauna (S20). The area dividing process S20 is performed by the initial partition unit 72 and the subsequent partition unit 74. The region division processing is started from the low resolution image, and the division processing using the pixel values of the enlarged pixels constituting the low resolution image is first performed at least once, and thereafter, the original pixel and the enlarged pixels constituting the ortho image data 40 Alternatively, the partition process using the pixel value of only the original pixel is performed at least once. Specifically, the initial partition unit 72 divides the low-resolution image into regions and generates a forest stand partition with a small scale (S22). The succeeding section 74 uses the low resolution image and the high resolution image together, and generates a forest fauna section having a larger scale by combining the forest fauna sections (S24).

  The region division process is performed so as to satisfy the spectrum condition 44 and the geometric condition 46. At that time, the specific gravity between the spectral condition 44 and the geometric condition 46, that is, the degree to which each contributes to the region division can be adjusted.

Here, the conditions at the time of generating the forest fauna division may be different for each layer of the region division processing. That is, Δh, which is a scale parameter, is generated when the initial enlarged pixels are combined to generate an initial forest fauna section, or when lower order forest fauna sections are combined to generate a higher order forest fauna section. It is set to allow the scale of the forest fauna compartment to gradually increase. Due to the increase of Δh, regarding the spectral condition 44, the similarity criterion (Δh _p ) for the similarity of spectral features for a plurality of enlarged pixels or forestry sections to be combined is relaxed as the region division processing becomes higher. Also, the geometric feature similarity criterion (Δh _t ) is relaxed with respect to the geometric condition 46, and as the region segmentation process becomes higher, forest facies sections with more complex shapes or larger areas are allowed. Bonding is promoted. On the other hand, since it is sufficient that the coupling is relaxed comprehensively in the spectral condition 44 and the geometric condition 46, the geometric condition 46 is changed in a direction to suppress the coupling to some extent while the coupling is relaxed by the spectral condition 44, and the simpler. It is also possible to set conditions that encourage the generation of forest-shaped sections of various shapes. For example, such adjustment is possible by changing the weights w _p and w _t in equation (1). Specifically, w _p is decreased, while w _t is increased and Δh _p is changed to Δh. This can be realized by making it difficult to contribute and making Δh _t easily contribute to Δh.

  Now, as already mentioned, in high resolution images, there are large variations in pixel values even within the same forest fauna due to the difference between the sun and shade of the forest, and it has been difficult to distinguish and accurately separate adjacent forest fauna. There was a problem that there was. On the other hand, the forest phase analysis system 2 reduces the resolution of the original image and performs at least the first one of the hierarchical region segmentation processes on the low resolution image. When the image is reduced in resolution, the variation in pixel values within the same forest phase is smoothed and alleviated, so the partition processing based on the low-resolution image is less affected by the difference in pixel values between the sun and the shade, and adjacent It is easy to distinguish different forest fauna. It should be noted that the effect can be expected to be higher when the partition processing at a low resolution is performed at an earlier stage of a plurality of hierarchical partition processing.

  On the other hand, information on the high resolution image is obtained in the final forest fauna compartment by performing the compartment processing using both the low resolution image and the high resolution image or only the high resolution image in the hierarchical compartment processing. Can be reflected.

  9 to 11 are images showing examples of processing of the forest fauna compartment generating unit 20, and the boundaries of the forest fauna compartments are drawn with black lines in the aerial photograph images of the same forest. FIG. 9 shows a result of processing by the initial partition unit 72, and shows an initial forest phase partition generated using a low resolution image. 10 and 11 show the results of processing by the subsequent partition unit 74, and the image of FIG. 11 shows the result of the region dividing process of higher floors than the image of FIG. That is, area division is performed hierarchically in the order of FIG. 9, FIG. 10, and FIG. Here, as described above, the forest stand section is generated by using both the low resolution image and the high resolution image. Specifically, in the case where the three layers shown in FIGS. 9 to 11 are all layers of the region dividing process for generating the forest fauna compartment, the forest fauna compartment of FIG. 9 is generated using the low resolution image, and FIG. 11 are generated using both the low-resolution image and the high-resolution image in the forest-facing zone, which are the processing results of the preceding region division, or only the high-resolution image. And the forest fauna compartment of FIG. 11 becomes the forest fauna compartment finally extracted.

  The forest block data 100 finally extracted from the target area by the processing of the forest block section generation unit 20 described above is used by the texture analysis unit 22, the spectrum analysis unit 24, and the forest phase division map generation unit 30.

  Now, the description returns to FIG. The texture analysis unit 22 and the spectrum analysis unit 24 extract the image feature information data 102 for each forest phase section at the time T2 from the ortho image data 40 and the forest phase section data 100. FIG. 12 is a schematic flowchart for explaining the feature information data 102 extraction processing by the texture analysis unit 22 and the spectrum analysis unit 24.

  The forest phase analysis system 2 extracts texture information from the ortho image data 40 by the texture analysis unit 22 (S40, S50, S60). Specifically, the texture analysis unit 22 extracts the sun / shade distribution pattern by the sun / shade distribution pattern information extraction unit 80 (S40), and extracts the sun / shade boundary pattern by the sun / shade boundary pattern information extraction unit 82. (S50) Further, the LBP image information extraction unit 84 extracts an LBP image in which a detailed pattern structure pattern in the original image appears (S60), and obtains texture information.

  Further, the forest phase analysis system 2 normalizes the ortho image data 40 by the spectrum analysis unit 24, and extracts spectral feature information from the normalized image (S70). Further, the spectrum analysis unit 24 uses the sunny / shade distribution pattern extracted by the sunny / shade distribution pattern information extracting unit 80 to extract spectral feature information only from the sunny part where the forest spectral features can be satisfactorily displayed (S80). .

  Then, the texture analysis unit 22 and the spectrum analysis unit 24 acquire image feature information for each forest phase section extracted by the forest phase section generation unit 20 (S90), and generate feature information data 102. The feature information data 102 is used by the statistical distribution creation unit 26 and the forest fauna division map generation unit 30.

  Returning again to FIG. The statistical distribution creation unit 26 uses the feature map data 42 for the forest phase at the time T1 and the feature information data 102 for the image for each forest phase at the time T2 to generate an image of each region of interest set for each forest phase in the original image at the time T2. Find the statistical distribution of feature information. The teacher data determination unit 28 generates teacher data 106 for each forest phase based on the statistical distribution data 104 output from the statistical distribution creation unit 26.

  FIG. 13 is a schematic diagram of a statistical distribution related to a region of interest corresponding to one forest phase. The horizontal axis corresponds to a variable represented by the feature amount X related to feature information, and the vertical axis corresponds to the frequency Y. Here, for convenience of illustration, the statistical distribution 108 is a one-dimensional distribution having one variable, but the statistical distribution creating unit 26 may obtain a multi-dimensional statistical distribution having a plurality of variables. For example, the variables are the forest fauna corresponding to the attention area by the user, and the forest fauna corresponding to the forest change that is expected to occur between the times T1 and T2 in the attention area, specifically the felling or wind damage damage. It is set to a single feature value or a combination of a plurality of feature values suitable for discriminating. Each feature information calculated by the texture analysis unit 22 and the spectrum analysis unit 24 described above is suitable for the discrimination, and a statistical distribution variable can be defined by some or all of the plurality of types of feature information. .

The statistical distribution of feature values for element regions (forest stand sections, pixels, etc.) with common forest phases is basically a distribution that accumulates near the average value to form a peak. For example, a normal distribution is assumed. be able to. As already described, it can be assumed that the number of change areas in which the forest fauna changes from the time T1 in the target area is smaller than the area that does not change. Therefore, the statistical distribution 108 for each region of interest approximates the distribution of feature values of the forest fauna corresponding to the region of interest. That is, there is a high probability that the element region of the forest fauna has a feature quantity near the maximum peak in the statistical distribution 108. Therefore, the teacher data determination unit 28 determines the feature amount of the image in the position of the maximum peak in the statistical distribution 108 obtained for the attention area corresponding to a certain forest aspect or in a predetermined vicinity range thereof (for example, the range R _P in FIG. 13). Are extracted as feature data of the forest phase at time T2, and are used as teacher data when performing the forest phase classification of the original image. For example, the teacher data determination unit 28 can estimate the average m and the variance σ of the distribution of the feature amount of the forest fauna from the statistical distribution 108 and determine the teacher data based on this.

  The forest fauna division map generation unit 30 acquires feature information from the feature information data 102 for each forest fauna compartment given by the forest fauna compartment data 100, classifies the feature information by the teacher data 106, and discriminates the forest fauna. As a result, a forest phase map (forest phase map data 110) for time T2 is generated. For example, when average m and variance σ are given as teacher data for each forest facies, the forest facies section diagram generation unit 30 calculates the likelihood of each forest facies with respect to the feature quantity X of the forest fauna compartment using m and σ, The forest type section is classified into the forest type with the maximum likelihood.

  The forest phase change detection unit 32 compares the existing forest phase map (forest phase map data 42) at time T1 with the forest phase map (forest phase map data 110) at time T2 generated by the forest phase map generation unit 30. Then, a change in the forest fauna in the target area is detected, and a forest fauna change data 112 is generated.

  Note that texture information other than the above three types can also be used. For example, an image texture extracted by wavelet analysis can be used. Specifically, the forest phase can be determined using the high-frequency component information extracted by the wavelet analysis as texture information together with the spectral feature information of the image.

  In the present embodiment, the ortho image data 40 is an aerial photograph, but the present invention can also be applied to forest facies analysis using other high resolution images such as satellite photographs. In the present embodiment, an ortho image is described as an example, but it is not always necessary to use an ortho image, and an image taken from the sky can be used as appropriate.

[Second Embodiment]
In the forest phase analysis system of the second embodiment, the same components as those of the first embodiment are denoted by the same reference numerals, and description thereof is omitted. Hereinafter, differences between the forest facies analysis system 200 of the second embodiment and the forest facies analysis system 2 of the first embodiment will be described.

  FIG. 14 is a block diagram showing a schematic configuration of a forest facies analysis system 200 according to the second embodiment. The difference between the forest facies analysis system 200 and the forest facies analysis system 2 is in what conditions the forest facies division is performed. In the first embodiment, the forest facies section generation unit 20 generates a forest facies section by performing region division processing so as to satisfy the spectrum condition 44 for the spectral feature quantity and the geometric condition 46 for the geometric feature quantity. On the other hand, in the forest analysis system 200 of the second embodiment, the object before and after the region merging, that is, the color, shape, texture, and area of the pixel of the degraded image or the forest phase partition are used as the feature amount for the partition, and the colors for these Region division processing is performed so as to satisfy the condition 202, the shape condition 204, the texture condition 206, and the area condition 208, and forest stand sections are generated.

  The color condition 202, the shape condition 204, the texture condition 206, and the area condition 208 are conditions that must be satisfied by a forest section that is an object region generated by dividing an ortho image (ortho image data 40). The color condition 202 defines a condition regarding the pixel value of the ortho image in the forest block. Incidentally, “color” in “color condition” means a pixel value of an ortho image and is used in a broader sense than spectral information composed of RGB information. Specifically, the “color” here corresponds to the spectral feature amount of the first embodiment, and for example, the pixel value of a multispectral image including NIR information can also be included in the color concept here. The shape condition 204 defines a condition regarding the shape of the forest fauna compartment. The texture condition 206 defines a condition related to the image texture of the forest fauna section. The image texture is evaluated by the texture feature amount. The area condition 208 defines a condition relating to the area of the forest land section. In addition, the shape and area of the forest fauna compartment are geometric feature quantities of the forest fauna compartment.

  The initial partition unit 72 determines whether or not a region composed of a plurality of pixels adjacent to each other is a primary forest phase partition, the similarity of pixel values of these pixels, the similarity of the image texture of the region, the shape of the region, It is determined based on the similarity of geometric features such as area. Here, the similarity of the pixel values between the regions to be combined or between the regions before and after the combination can be determined based on a feature amount such as a statistic such as an average value of the pixel values.

  The subsequent partition unit 74 performs sequential partition processing as in the first embodiment. In the present embodiment, the sequential partition processing is performed on the plurality of low-order forest facies partitions adjacent to each other, the color condition 202 regarding the similarity of pixel values in them, and the shape condition on the higher-order forest tract partitions obtained by combining them. Based on 204, texture condition 206 and area condition 208, it is determined whether or not to combine these lower-order forest fauna sections.

Similar to the first embodiment, a method based on region merging is used for image region division. In this embodiment, the change in object heterogeneity Δh due to region merging includes a change in color heterogeneity Δh _color before and after merging, a change in shape heterogeneity Δh _shape , a change in _texture heterogeneity Δh _texture , The area heterogeneity change _Δhaera is calculated by the following equation.

Here, w _color is the weight of the heterogeneity of the color, w _shape is the weight of the heterogeneity of the shape, w _texture is the weight of the heterogeneity of the texture, and w _area is the weight of the heterogeneity of the area.

The change in _color heterogeneity Δh _color before and after merging, the change in shape heterogeneity Δh _shape , the change in texture heterogeneity Δh _texture , and the change in area heterogeneity _Δhaera are calculated by the following equations, for example.

Here, n _ab is the number of pixels of the new object after merging, and n _a and n _b are the number of pixels of the two objects before merging. CI _ab is the color information index (for example, pixel value) of the object after merging, CI _a and CI _b are the color information indices of the two objects before merging, and SI _ab is the shape information index of the object after merging (for example, , Smoothness), SI _a and SI _b are the shape information indices of the two objects before merging, TI _ab is the texture information index of the object after merging (for example, the standard deviation value of the pixel value), and TI _a and TI _b are The texture information index of the two objects before merging, AI _ab is the area information index (for example, area value) of the object after merging, and AI _a and AI _b are the area information indices of the two objects before merging.

As shown in the equation (6), each of the color condition, the shape condition, the texture condition, and the area condition contributes to the determination of the combination of pixels in the initial partition section 72 or the determination of the combination of low-order forest facies sections in the subsequent partition section 74. The ratio can be adjusted by weights w _color , w _shape, w _texture, w _area . Here, one or both of the initial partition unit 72 and the subsequent partition unit 74 are configured based on any one or a combination of a color condition, a shape condition, a texture condition, and an area condition in the generation determination of the forest block section. It can also be.

  2,200 Forest phase analysis system, 4 arithmetic processing device, 6 storage device, 8 input device, 10 output device, 20 forest phase section generation unit, 22 texture analysis unit, 24 spectrum analysis unit, 26 statistical distribution creation unit, 28 teacher data decision , 30 Forest facies map generation unit, 32 Forest facies change detection unit, 40 Ortho image data, 42 Forest phase map data at time T1, 44 Spectral conditions, 46 Geometric conditions, 70 Low resolution image generation unit, 72 Initial partition unit, 74 Subsequent partition section, 80 Hinata / shade distribution pattern information extraction section, 82 Hinata / shade boundary pattern information extraction section, 84 LBP image information extraction section, 100 Forest phase partition data, 102 Feature information data, 104 Statistical distribution data, 106 Teacher data, 110 Seasonal T2 forest phase map data, 112 Forest phase change data, 202 color conditions , 204 shape condition, 206 texture condition, 208 area condition.

Claims (9)

Corresponding to the area of each forest fauna in the known forest fauna division map in the first period of the target area including the forest, the target area is set to the original image taken from the sky in the second period, Statistical distribution creating means for obtaining a statistical distribution for the feature information of the image extracted for each predetermined element region in the attention region of each forest phase;
The condition corresponding to the non-change region based on the statistical distribution in the target region under the condition that the non-change region in which the forest fauna does not change between the first time period in each of the target regions. Teacher data determining means for determining teacher data related to the feature information of the forest minister;
Forest stand map generation means for determining the forest stand in each element area of the original image from the teacher data determined for each forest stand, and generating a forest block map in the second period;
A forest phase analysis apparatus characterized by comprising: In the forest phase analysis apparatus according to claim 1,
A forest fauna characterized by comprising a forest fauna change detection means for comparing the forest fauna segment map in the first period and the forest fauna segment map in the second period to detect a change in the forest fauna in the target area. Analysis device. In the forest fauna analysis apparatus according to claim 1 or 2,
A forest fauna compartment generating means for generating and outputting the forest fauna compartment of the forest by dividing the target area into a plurality of regions based on the similarity of one or more kinds of compartment features of the original image;
The element region in the statistical distribution creation means and the forest fauna division map creation means is the forest fauna section;
Forest phase analysis device characterized by In the forest phase analysis apparatus according to claim 3,
From the original image, having an image deterioration means for generating a deteriorated image in which the resolution is reduced with a region composed of a plurality of adjacent pixels of the original image as a pixel,
The forest fauna compartment generating means includes:
An initial partition means for dividing the degraded image into a plurality of regions based on the similarity of the one or more types of the feature values for the partition, and generating a forest fauna partition;
The adjacent forest fauna compartments are combined based on the similarity of the one or more types of the feature values for the compartments to generate a new forest fauna compartment once or multiple times, and the forest in the target area Subsequent compartment means for outputting the forest fauna compartment,
Have
The subsequent partition means performs at least one sequential partition processing based on the partition feature of the original image or the partition feature of each of the original image and the degraded image;
Forest phase analysis device characterized by In the forest fauna analysis apparatus according to claim 3 or claim 4,
Texture information extracting means for extracting texture information from the original image;
The statistical distribution creating means obtains the statistical distribution using the texture information extracted from each forest fauna compartment output from the forest fauna compartment generating means as the feature information;
Forest phase analysis device characterized by In the forest phase analysis apparatus according to claim 5,
Spectral information extracting means for extracting spectral information from the original image;
The statistical distribution creating means obtains the statistical distribution using the texture information and the spectrum information extracted from each forest fauna compartment output from the forest fauna compartment generating means as the feature information,
Forest phase analysis device characterized by In the forest fauna analysis apparatus according to claim 3 or claim 4,
Spectral information extracting means for extracting spectral information from the original image;
The statistical distribution creating means obtains the statistical distribution using the spectral information extracted from each forest fauna compartment output from the forest fauna compartment generating means as the feature information;
Forest phase analysis device characterized by Corresponding to the area of each forest fauna in the forest fauna division map in the first period of the target area including the forest, a target area is set in the original image obtained by capturing the target area from the sky in the second period. A statistical distribution creating step for obtaining a statistical distribution of image feature information extracted for each predetermined element region in the attention region;
The condition corresponding to the non-change region based on the statistical distribution in the target region under the condition that the non-change region in which the forest fauna does not change between the first time period in each of the target regions. A teacher data determination step for determining teacher data related to the feature information of the forest minister;
A forest facies section map generation step for determining a forest facies in each element region of the original image from the teacher data determined for each forest facies, and generating a forest facies section map in the second period;
The forest phase analysis method characterized by having. A program for causing a computer to perform forest fauna analysis in a target area including forests,
Corresponding to the area of each forest fauna in the forest fauna division map in the first period of the target area including the forest, a target area is set in the original image obtained by capturing the target area from the sky in the second period. Statistical distribution creating means for obtaining a statistical distribution for image feature information extracted for each predetermined element region in the region of interest;
The condition corresponding to the non-change region based on the statistical distribution in the target region under the condition that the non-change region in which the forest fauna does not change between the first time period in each of the target regions. Teacher data determining means for determining teacher data related to the feature information of the forest minister; and
Forest stand section generation means for discriminating the forest stand in each element area of the original image from the teacher data determined for each forest stand, and generating a forest stand section map in the second period;
A program characterized by functioning as

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