JP2021047064A - Water-containing state estimation device, water-containing state estimation program, and water-containing state estimation method - Google Patents

Abstract

To provide a water-containing state estimation device and a water-containing state estimation program which solve a problem of a prior art, in other words, can efficiently grasp a water-containing state of an object soil compared to the prior art, and can grasp the water-containing state of the object soil without requiring a worker to approach the object soil, and a water-containing state estimation method using the same.SOLUTION: A water-containing state estimation device is a device for estimating a water-containing state of an object soil by using a result obtained by photographing a sample soil whose water-containing state is known with a hyper-spectral camera, and includes correlation calculation means and two-wavelength relational expression calculation processing, and water-containing state estimation means. The water-containing state estimation means is means for estimating the water-containing state of the object soil on the basis of object soil data composed of a first spectral intensity at a first wavelength and a second spectral intensity at a second wavelength obtained by photographing the object soil by a spectral camera, and the two-wavelength relational expression.SELECTED DRAWING: Figure 1

Description

The present invention is a technique for measuring the water content of soil, and more specifically, using the relationship of spectral intensities of two predetermined wavelengths from the results of photographing a sample soil having a known water content with a hyperspectral camera. It relates to a device and a program for estimating the water content of the target soil, and an estimation method using these.

Japan is known as a country where earthquakes occur frequently, and in recent years, major earthquakes such as the Tohoku-Pacific Ocean Earthquake, the Hyogo-ken Nanbu Earthquake, and the Niigata-ken Chuetsu Earthquake have occurred, and each time they have suffered enormous damage. In particular, the Great East Japan Earthquake caused immeasurable damage from the tsunami and also caused an accident in which the reactor of the Fukushima nuclear power plant was damaged. A large amount of radioactive material released due to the nuclear reactor accident was advected and diffused over a wide area in the form of aerosols, etc., and as a result of falling and depositing on the ground due to rainfall, it had a great impact on the living environment of local residents.

Against this background, the Act on Special Measures Concerning Radioactive Contamination was enforced, and so-called decontamination measures such as removal of soil, vegetation, and debris on which radioactive substances were deposited were promoted. According to this law, which aims to quickly reduce the impact on human health and living environment, radioactive waste (specified waste) is required to be stored and disposed of in accordance with the standards specified by the Ordinance of the Ministry of the Environment. .. According to this ministry ordinance, for example, radioactive waste is stored according to its radioactivity concentration, and those with a medium radioactivity concentration (more than 8,000 Bq / kg and less than 100,000 Bq / kg) are managed disposal sites. It is stipulated that those with a high radioactivity concentration (more than 100,000 Bq / kg) should be stored in an interim storage facility.

Normally, when landfilling the removed soil in an intermediate storage facility, the removed soil is sprinkled with a dump truck or the like, the sprinkled removed soil is spread with a bulldozer or the like, and the spread removed soil is spread out. It is performed by a series of operations such as compaction with a vibrating roller or the like. However, depending on the water content (moisture content ratio and water content) of the removed soil, dust due to air drying may be scattered from the surface of the removed soil during the sprinkling work or after the completion of compaction. Since such dust can be one of the factors of external exposure of workers and the like, it is necessary to grasp the water content state of the removed soil. In general, the soil removed in the interim storage facility is reclaimed over a wide area, so it is desirable to measure the water content efficiently, such as by measuring from the sky, and it is also required to measure the water content remotely.

As a method of measuring the water content of soil by remote control, a measurement method using a spectroscope (spectral camera) can be considered. For example, in Patent Document 1, a hyperspectral camera having a wavelength of 400 to 1,000 nm is used, and reference spectrum data that is close to the acquired spectrum data is specified by the SAM (Spectral Angle Mapper) method and associated with the reference spectrum data. We are proposing a technique for determining the water content of the target soil based on the attribute information (moisture content, soil type, etc.).

Japanese Unexamined Patent Publication No. 2015-40851

According to Patent Document 1, the water content can be determined without directly touching the target soil, that is, without contact, and since the hyperspectral camera is used, the water content can be determined relatively accurately. On the other hand, since a hyperspectral camera is used, there is a disadvantage that it is not possible to efficiently determine a wide range of soil. Normally, a hyperspectral camera is basically installed on the ground using a tripod, etc., and it takes a considerable amount of time (several tens of seconds to several minutes) to make one measurement. Compared to the method of measuring a wide range from the sky, the measurement efficiency is significantly inferior.

Further, it can be pointed out that the technique of Patent Document 1 requires an operator to enter the vicinity of the target soil in order to install the hyperspectral camera. For example, when measuring the water content of the removed soil landfilled in the interim storage facility, there is no problem if the water content is sufficiently maintained, but if the surface of the removed soil is dry, dust will scatter and the operator will be able to do so. May be exposed to the outside.

The object of the present invention is to solve the problems of the prior art, that is, the water content state of the target soil can be grasped more efficiently than the prior art, and the operator does not approach the target soil. It is to provide a water content estimation device and a water content estimation program capable of grasping the water content state of the target soil, and a water content estimation method using these.

According to the present invention, a wavelength band required for estimating the water content of the target soil is set in advance, and the water content is measured more efficiently than before by using a spectrum camera that measures the spectral intensity of the wavelength band. It was made with a focus on the point of doing so, and it was an invention made based on an unprecedented idea.

The water content estimation device of the present invention is a device that estimates the water content of the target soil based on the results obtained by photographing the sample soil whose water content (water content ratio and water content) is known with a hyperspectral camera. , It is equipped with a correlation degree calculation means, a two-wavelength relational expression calculation process, and a water content estimation means. Of these, the correlation degree calculation means uses the first spectral intensity at the first wavelength and the second wavelength (wavelength different from the first wavelength) based on the spectral data obtained from a plurality of types of sample soils having different water content. The second spectral intensity is extracted, and the first spectral intensity and the second spectral intensity obtained from the same water-containing sample soil are used as a set of sample soil data, and the sample soil data related to a plurality of water-containing sample soils are used. This is a means for determining the degree of correlation between the first spectral intensity and the second spectral intensity. Further, the two-wavelength relational expression calculation process is a means for obtaining a two-wavelength relational expression representing the relationship between the first spectral intensity and the second spectral intensity when the degree of correlation exceeds a predetermined correlation threshold value. Then, the water content estimation means is based on the target soil data consisting of the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength obtained by photographing the target soil with a spectrum camera, and the two-wavelength relational expression. It is a means to estimate the water content of the target soil.

The water content estimation device of the present invention further includes an unmanned aerial vehicle, a multi-lens spectrum camera mounted on the unmanned aerial vehicle, and an observation means having control means for controlling the unmanned aerial vehicle and the multi-lens spectrum camera. It can also be. In addition, this multi-lens spectrum camera is provided with two or more lenses, and each lens is provided with a filter for acquiring spectral intensity at a desired wavelength. In this case, the water content estimation means estimates the water content of the target soil by a two-wavelength relational expression using the target soil data obtained by photographing the target soil with a multi-lens spectrum camera while the unmanned vehicle is flying. ,

The water content estimation device of the present invention may also estimate the water content of the target soil when the difference between the target soil data and the two-wavelength relational expression is within a predetermined allowable range.

The water content estimation device of the present invention may further include a two-wavelength relational storage means. This two-wavelength relational expression storage means is a means for storing two or more two-wavelength relational expressions composed of different combinations of two wavelengths. In this case, the water-containing state estimation means selects the first spectral intensity and the second spectral intensity from the spectral intensities of three or more wavelengths obtained by photographing the target soil with a spectrum camera, and the first spectral intensity. And the two-wavelength relational expression related to the second spectral intensity are read out from the two-frequency relational expression storage means, and the target soil data (selected first spectrum intensity and second spectral intensity) are compared with the read out two-wavelength relational expression. When the difference between the target soil data and the two-wavelength relational expression is outside the predetermined permissible range, the other first spectral intensity and the second spectral intensity are selected, and when it is within the permissible range, the water content of the target soil is contained. Estimate the state.

The water content estimation program of the present invention is a program that causes a computer to execute a function of estimating the water content of the target soil based on the result obtained by photographing the sample soil whose water content is known with a hyperspectral camera. It is equipped with a function to cause a computer to execute a correlation degree calculation process, a two-wavelength relational expression calculation process, and a water content estimation process. Of these, the correlation degree calculation process extracts the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength based on the spectral data obtained from a plurality of types of sample soils having different water content, and also extracts the second spectral intensity at the second wavelength. The first spectral intensity and the second spectral intensity obtained from the same water-containing sample soil are used as a set of sample soil data, and the first spectral intensity and the second spectrum are obtained from the sample soil data related to a plurality of water-containing sample soils. This is a process for determining the degree of intensity correlation. The two-wavelength relational expression calculation process is a process for obtaining a two-wavelength relational expression representing the relationship between the first spectral intensity and the second spectral intensity when the degree of correlation exceeds the correlation threshold value. Then, the water content estimation process is based on the target soil data consisting of the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength obtained by photographing the target soil with a spectrum camera, and the two-wavelength relational expression. This is a process for estimating the water content of the target soil.

The water content estimation method of the present invention is a method of estimating the water content of the target soil based on the result obtained by photographing the sample soil whose water content is known with a hyperspectral camera, and correlates with the sample soil photographing step. It is equipped with a degree calculation process, a two-wavelength relational expression calculation process, a target soil photography process, and a water content estimation process. Of these, in the sample soil photographing step, a plurality of types of sample soil having different water content states are photographed with a hyperspectral camera, and spectrum data is acquired for each water content state. Further, in the correlation degree calculation step, two different types of wavelengths are selected as the first wavelength and the second wavelength, and among the spectral data acquired for each water-containing state, the first spectral intensity at the first wavelength and the second wavelength are used. The second spectral intensity is extracted, and the first spectral intensity and the second spectral intensity related to the sample soil in the same water-containing state are used as a set of sample soil data, and the first is obtained from the sample soil data related to the sample soil in a plurality of water-containing states. The degree of correlation between the spectral intensity and the second spectral intensity is determined. In the two-wavelength relational expression calculation step, when the degree of correlation exceeds the correlation threshold value, a two-wavelength relational expression representing the relationship between the first spectral intensity and the second spectral intensity is obtained. In the target soil photographing step, the target soil is photographed with a spectrum camera to acquire the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength. Then, in the water-containing state estimation step, the water-containing state of the target soil is estimated based on the target soil data consisting of the first spectral intensity and the second spectral intensity acquired in the target soil photographing step and the two-wavelength relational expression.

The water content estimation method of the present invention can also be a method using an observation means. In this case, in the target soil photographing process, after attaching a filter to the lens so as to photograph at the first wavelength and the second wavelength related to the two-wavelength relational expression, the target soil is photographed by a multi-lens spectrum camera while the unmanned flying object is flying. To shoot.

The water content estimation device, the water content estimation program, and the water content estimation method of the present invention have the following effects.
(1) Since the spectrum data is acquired in the preset wavelength band, the work can be completed in a shorter time than the measurement by the conventional hyperspectral camera. In addition, it is possible to more efficiently estimate the water content of the target soil by measuring with an unmanned flying object equipped with this multi-lens spectrum camera using a multi-lens spectrum camera that acquires preset spectrum data. it can.
(2) By using an unmanned aerial vehicle equipped with a multi-lens spectrum camera, the water content of the target soil can be estimated by remote control. As a result, for example, even when measuring the water content of the removed soil landfilled in the interim storage facility, it is not necessary for the worker to enter the vicinity of the site, that is, there is no risk of the worker being exposed to the outside.
(3) It can be used for compaction water content control of high-concentration removed soil and watering control to prevent dust scattering.
(4) By acquiring the spectral data of the first wavelength and the second wavelength wavelength, which are effective for estimating the water content ratio with the multi-lens spectrum camera, the hyperspectral camera is used instead of the hyperspectral camera in the measurement of the target soil. The water content of the target soil can be estimated with an accuracy comparable to that of the measurement.

The block diagram which shows the main structure of the water content estimation apparatus of this invention. Front view showing a multi-lens spectrum camera. A flow chart showing the flow of "pre-processing" from obtaining a two-wavelength relational expression from a plurality of types of spectral data. A flow chart showing the flow of "observation processing" until the target soil is actually photographed on site and its water content is estimated. A model diagram illustrating multispectral data obtained by photographing a sample soil with a hyperspectral camera. (A) is a plan view showing each region obtained by dividing the sample soil, and (b) is a graph showing a spectral pattern generated for each region. The model figure which shows the sample soil of 4 kinds of water content ratios with the same soil quality, and the spectral pattern for 16 regions generated for each sample soil. A scatter plot in which sample soil data obtained from each spectral pattern is plotted in a coordinate system having the first spectral intensity on the horizontal axis and the second spectral intensity on the vertical axis. (A) is a graph in which sample soil data is plotted by selecting a wavelength set having a first wavelength of 400 nm and a second wavelength of 450 nm, and (b) is a graph in which the first wavelength is 380 nm and the second wavelength is 1,000 nm. A graph in which sample soil data is plotted by selecting the wavelength set to be used. (A) is a scatter plot in which sample soil data is plotted by selecting a correlated wavelength set having a first wavelength of 485 nm and a second wavelength of 500 nm, and (b) has a first wavelength of 450 nm and a second wavelength of 475 nm. A scatter plot in which sample soil data is plotted by selecting a correlated wavelength set. A flow chart showing the flow of "observation processing" when the difference between the target soil data and the two-wavelength relational expression is taken into consideration. A model diagram showing the difference obtained by the perpendicular line from the target soil data to the regression line. A flow chart showing the flow of "observation processing" when estimating the water content ratio of the target soil after selecting the optimum two-wavelength relational expression. The flow chart which shows the main processing flow of the water content estimation program of this invention. The flow chart which shows the flow of the main process of the water content estimation method of this invention.

An example of the water content estimation device, the water content estimation program, and the embodiment of the water content estimation method of the present invention will be described with reference to the drawings.

1. 1. Overall Overview In the present invention, by photographing the target soil (hereinafter referred to as "target soil") with a spectrum camera (hereinafter referred to as "observation spectrum camera"), the "water content" such as the water content ratio and the water content is "water content". It is a technology that can estimate the "state". As the observation spectrum camera used in the present invention, it is sufficient if the spectrum intensity at a specific wavelength can be acquired, that is, it is not necessary to use a hyperspectral camera that requires a considerable amount of time for one measurement. In this way, since a simple spectrum camera for observation is used, for example, it can be mounted on an unmanned aerial vehicle (UAV), and even a wide range of target soil can be efficiently photographed. That's why.

The "two-wavelength relational expression" is used to estimate the water content of the target soil using an observation spectrum camera, that is, from limited information. This two-wavelength relational expression is obtained based on the spectral data obtained by photographing soil having a known water content (hereinafter referred to as "sample soil") with a hyperspectral camera, and is obtained from two selected soils. It represents the relationship between the respective spectral intensities at wavelength (hereinafter referred to as "wavelength set"). More specifically, from the spectral data obtained for the same sample soil, the spectral intensity at one wavelength of the wavelength set (hereinafter, referred to as "first wavelength") (hereinafter, "first spectral intensity"). The spectral intensity (hereinafter referred to as "second spectral intensity") at the other wavelength of the wavelength set (hereinafter referred to as "second wavelength") is extracted, and these first spectral intensity and second spectrum intensity are extracted. The two-wavelength relational expression expresses the relationship between the sample soil data obtained from a plurality of types of sample soil by treating the spectral intensity as a set of data (hereinafter referred to as "sample soil data").

The inventors of the present invention have found that a specific wavelength set exists for each sample soil, that is, a specific first spectrum intensity and a second spectrum intensity show an extremely high correlation. Note that sample soils of different types (soil quality, etc.) have different specific wavelength sets, and sample soils of the same type but different water content also have different specific wavelength sets. Then, by using a two-wavelength relational expression expressing the relationship between the specific first spectral intensity and the second spectral intensity, the water content state of the target soil can be estimated even with limited information obtained from the observation spectrum camera. I focused on what I could do.

2. Water content estimation device An example of the water content estimation device of the present invention will be described with reference to the drawings. The water content estimation program of the present invention causes a computer to perform some processing of the water content estimation device of the present invention, and the water content estimation method of the present invention uses the water content estimation device of the present invention. It is a method of estimating the water content state by using it. Therefore, first, the water content estimation device of the present invention will be described, and then the water content estimation program of the present invention and the water content estimation method of the present invention will be described. Further, for convenience, here, an example in which the "water content state" is used as the water content ratio will be described.

FIG. 1 is a block diagram showing a main configuration of the water content estimation device 100 of the present invention. As shown in this figure, the water content estimation device 100 of the present invention is configured to include the correlation degree calculation means 101, the two-wavelength relational expression calculation process 102, and the water-containing state estimation means 103, and further includes the two-wavelength relational expression storage means 104 and the like. It can also be configured to include the spectrum data storage means 105 and the observation means 200.

Among the main elements constituting the water content estimation device 100, the correlation degree calculation means 101, the two-wavelength relational expression calculation process 102, and the water content estimation means 103 can be manufactured as dedicated ones or are general-purpose computer devices. Can also be used. This computer device is equipped with a processor such as a CPU, a memory such as a ROM and a RAM, an input means such as a mouse and a keyboard, and a display, and includes a personal computer (PC), a tablet PC such as an iPad (registered trademark), and a smartphone. It can be configured by including a mobile terminal or the like.

The two-wavelength relational storage means 104 and the spectrum data storage means 105 can be constructed in the memory of a computer device that implements, for example, the correlation degree calculation means 101, or are connected by a local network (LAN: Local Area Network). It can be built on a built-in database server, or it can be a cloud server that stores data via the Internet (that is, wireless communication or wired communication).

The observation means 200 includes at least an observation spectrum camera. This observation spectrum camera can acquire a specific wavelength set, that is, a specific first spectrum intensity and a second spectrum intensity, and for example, a multi-lens spectrum camera 210 as shown in FIG. 2 is used. Can be done. The multi-lens spectrum camera 210 includes two or more lenses 211 (five in the figure) and a filter 212. The filter 212 is a so-called adjusting means for acquiring the spectral intensity at a specific wavelength, and is detachably attached to each lens 211. In other words, by selecting the filter 212 attached to the lens 211, the spectral intensity at a desired wavelength can be obtained.

The observation means 200 can also be configured to include an unmanned aerial vehicle and a control means (controller). By mounting an observation spectrum camera on an unmanned aerial vehicle and remotely controlling the unmanned aerial vehicle and the observation spectrum camera with control means, it is possible to photograph the target soil from the air. Therefore, the observation spectrum camera should be mounted underneath the unmanned aerial vehicle.

Hereinafter, the main treatments of the water content estimation device 100 will be described in detail with reference to FIGS. 3 and 4. The water content estimation device 100 is used for "pre-processing" to obtain a two-wavelength relational expression from a plurality of types of spectral data and "observation processing" to actually photograph the target soil on site and estimate its water content ratio. It can be roughly divided, FIG. 3 is a flow chart showing the main flow of preprocessing, and FIG. 4 is a flow chart showing the main flow of observation processing. In these flow charts, the central column shows the processing to be performed, the left column shows what is necessary for the processing, and the right column shows what is generated from the processing.

First, based on the spectrum data stored in the spectrum data storage means 105 (FIG. 1), a spectrum pattern having a wavelength on the horizontal axis and a spectrum intensity on the vertical axis is generated (Step 11 in FIG. 3). The spectrum data prepared for generating the spectrum pattern here is multispectral data obtained as a result of photographing the sample soil with a hyperspectral camera. FIG. 5 is a model diagram for explaining the multispectral data obtained by photographing the sample soil with a hyperspectral camera. As shown in this figure, the hyperspectral camera can obtain spectral intensities at a large number of wavelengths λ (151 bands in the figure) for each pixel, and as a result, the two axes of the planar image (X-axis in the figure-). It is possible to acquire multispectral data arranged in a three-dimensional space having a wavelength axis (λ axis in the figure) orthogonal to the Y axis). In the present invention, a conventionally used hyperspectral camera can be adopted.

A plurality of multi-spectral data are acquired from the same sample soil, and a plurality of types of spectral patterns are generated accordingly. For example, as shown in FIG. 6 (a), the sample soil is divided into 16 region RGs, multispectral data is acquired in each region RG, and the number of divided region RGs as shown in FIG. 6 (b) ( In this case, it is advisable to generate a spectral pattern only for 16). Further, the spectral pattern is generated for a plurality of sample soils having the same soil quality but different water content, and the plurality of sample soils. For example, in FIG. 7, four types of sample soils having the same soil quality and water content ratios (1.56%, 4.37%, 16.61%, 21.14%) are prepared, and the spectral patterns of each of the 16 region RGs are prepared. Has been generated. In FIG. 3, m kinds of sample soils having a water content ratio are prepared, and one sample soil is divided into n region RGs to generate n × m spectral patterns.

When a plurality of types of spectral patterns are generated, a wavelength set (first wavelength and second wavelength) is selected (Step 12 in FIG. 3), and sample soil data is extracted from each spectral pattern (Step 13 in FIG. 3). As described above, the sample soil data is a set of data of the first spectral intensity and the second spectral intensity, and the first spectral intensity is the spectral intensity at the first wavelength and is the second spectral intensity. Is the spectral intensity at the second wavelength.

When the sample soil data is extracted from each spectral pattern, the “degree of correlation” is calculated by the correlation degree calculation means 101 (FIG. 1) (Step 14 in FIG. 3). Here, the degree of correlation is a value obtained as a result of performing a correlation analysis on the first spectral intensity and the second spectral intensity, and examples thereof include a correlation coefficient and coherence. For convenience, here, an example in which the "degree of correlation" is used as the correlation coefficient will be described.

For the correlation coefficient, for example, as shown in FIG. 8, a coordinate system having the first spectral intensity as the horizontal axis and the second spectral intensity as the vertical axis is set, and the sample soil obtained from each spectral pattern is set in this coordinate system. It is advisable to calculate after plotting the data. In this figure, for convenience, five sample soil data, namely (X ₁ , Y ₁ ) and (X ₂ , Y ₂ ), (X ₃ , Y ₃ ), (X ₄ , Y ₄ ), (X ₅ , Y). _{Only 5} ) is shown.

When the correlation coefficient is calculated, the correlation coefficient is compared with a predetermined threshold value (hereinafter, referred to as “correlation threshold value”) (Step 15 in FIG. 3). Depending on the selected wavelength set, there is a considerable correlation between the first and second spectral intensities, but depending on the combination of wavelength sets, there may be no correlation between the first and second spectral intensities. is there. FIG. 9A uses multispectral data obtained for sample soils A having different water content ratios, selects a wavelength set in which the first wavelength is 400 nm and the second wavelength is 450 nm, and uses the sample soil data. It is a plotted scatter diagram, and the relationship between the sample soil data shows that there is a considerable correlation between the first spectral intensity and the second spectral intensity in the wavelength set. On the other hand, FIG. 9B uses the multispectral data obtained for the same sample soil A as in FIG. 9A to select a wavelength set in which the first wavelength is 380 nm and the second wavelength is 1,000 nm. Then, it is a scatter diagram in which the sample soil data is plotted, but no considerable correlation is observed between the first spectral intensity and the second spectral intensity in the wavelength set.

Therefore, in order to select a wavelength set having a considerable correlation between the first spectral intensity and the second spectral intensity (hereinafter, referred to as “correlated wavelength set”), the correlation coefficient obtained from the wavelength set is compared with the correlation threshold. .. Then, when the correlation coefficient falls below the correlation threshold value (No in Step 15 in FIG. 3), the wavelength set is reselected (Step 12 in FIG. 3), and a series of processes (Step 13 to Step 15 in FIG. 3) are performed again. On the other hand, when the correlation coefficient exceeds the correlation threshold value (Yes in Step 15 in FIG. 3), the wavelength set is set as the “correlation wavelength set” and the process proceeds to the next step (Step 16 in FIG. 3). FIG. 10 is a scatter diagram in which sample soil data set as a correlated wavelength set are plotted, and FIG. 10 (a) uses _{multispectral data obtained for sample soils B 1 to} B ₅ having different water content ratios and uses the first wavelength. the 485 nm, a case in which a second wavelength 500 nm, (b) uses a multi-spectral data obtained for the sample soil _C 1 -C ₅ with different water content ratio, the first wavelength 450 nm, the second wavelength Is 475 nm, and it can be seen that there is a considerable correlation between the first spectral intensity and the second spectral intensity in each case.

When the correlation wavelength set is selected, the “two-wavelength relational expression” is calculated by the two-wavelength relational expression calculation process 102 (FIG. 1) (Step 16 in FIG. 3). This two-wavelength relational expression can be calculated as a regression line or a regression curve obtained based on the sample soil data related to the correlation wavelength set. The two-wavelength relational expression calculated here is stored in the two-wavelength relational expression storage means 104 (FIG. 1). Even if the correlated wavelength set is selected and the two-wavelength relational expression is calculated, a series of processes (Steps 13 to 16 in FIG. 3) can be performed after further changing the wavelength set (Step 12 in FIG. 3). That is, a plurality of two-wavelength relational expressions are calculated for a sample soil having a predetermined soil quality. Further, after changing the soil quality of the sample soil, a series of treatments (Steps 11 to 16 in FIG. 3) can be performed. That is, the two-wavelength relational expression is calculated for each sample soil of different soil quality. In these cases, a plurality of two-wavelength relational expressions are stored in the two-wavelength relational expression storage means 104.

When the two-wavelength relational expression is prepared by the pretreatment shown in FIG. 3, a desired correlation wavelength set is selected for photographing the target soil. At this time, it is advisable to select the correlation wavelength set after predicting the soil quality of the target soil and the approximate water content ratio. Then, an observation spectrum camera so that measurement can be performed with the first wavelength (hereinafter, referred to as "correlated first wavelength") and the second wavelength (hereinafter, referred to as "correlated second wavelength") constituting the selected correlated wavelength set. Adjust and actually shoot the target soil. For example, when the multi-lens spectrum camera 210 shown in FIG. 2 is used, the filter 212 corresponding to the first correlation wavelength is attached to any of the five lenses 211, and the filter 212 corresponding to the second correlation wavelength is attached. .. In the case where a plurality of two-wavelength relational expressions are calculated, two or more correlated wavelength sets are selected, and the observation spectrum camera is adjusted so that the measurement can be performed with the two types of correlated first wavelength and correlated second wavelength. You can also take a picture of the target soil after doing so.

When the spectral data of the target soil is obtained, the first spectral intensity at the correlation first wavelength and the second spectral intensity at the correlation second wavelength (hereinafter, the combination of these first spectral intensity and the second spectral intensity is referred to as "target soil data". ) Is extracted (Step 21 in FIG. 4). Then, the water content estimation means 103 (FIG. 1) estimates the water content ratio of the target soil based on the target soil data extracted here and the two-wavelength relational expression related to the selected correlation wavelength set (FIG. 4). Step22). For example, in the case shown in FIG. 10A, the sample soil data are arranged on a substantially straight line, and are arranged together for each water content ratio. Therefore, the regression line can be calculated as a two-wavelength relational expression from this scatter plot, and the water content ratio of the target soil can be estimated from the first spectral intensity (or the second spectral intensity) and the regression line in the target soil data. it can. Specifically, it is possible to estimate that if the first spectral intensity of the target soil data 550 a water content ratio of the target soil 6.07% (corresponding to the water content ratio of the sample soil B _2), the second spectral strength can be estimated water content ratio of the target soil if 400 11.45% (corresponding to a water content ratio of the sample soil B _3). Similarly, in the case shown in FIG. 10 (b), if the first spectral intensity of the target soil data is 1,000 to water content ratio of the target soil 4.43% (corresponding to a water content ratio of the sample soil C ₂₎ can be estimated, the second spectral intensity can be estimated with a water content ratio of the target soil if 600 5.93% (corresponding to the water content ratio of the sample soil C _3).

By the way, it is conceivable that the target soil data is not plotted on (or around) the two-wavelength relational expression (regression line or regression curve). Therefore, it is advisable to estimate the water content ratio of the target soil after considering the difference between the target soil data and the two-wavelength relational expression. FIG. 11 is a flow chart showing the flow of “observation processing” when the target soil data considers the difference from the two-wavelength relational expression. In this case, the water content estimation means 103 (FIG. 1) calculated the difference between the target soil data and the two-wavelength relational expression before estimating the water content ratio of the target soil (Step 22 in FIG. 11) (Step 23 in FIG. 11). ) Then, the difference is compared with the permissible range (Step 24 in FIG. 11). This difference can be obtained as the shortest distance between the target soil data and the two-wavelength relational expression. For example, if the two-wavelength relational expression is a regression line as shown in FIG. 12, a regression line (two-wavelength relationship) is obtained from the target soil data. The length of the perpendicular line up to (Equation) can be used as the difference.

When multiple two-wavelength relational expressions are calculated (that is, a correlation wavelength set is selected) and two or more correlation wavelength sets are selected and the target soil is photographed, the most suitable correlation wavelength set (that is, the two-wavelength relational expression) is selected. It is also possible to estimate the water content of the target soil after selection. FIG. 13 is a flow chart showing the flow of “observation processing” in the case of estimating the water content ratio of the target soil after selecting the optimum two-wavelength relational expression. In this case, first, one of the calculated correlation wavelength sets is selected (Step 25 in FIG. 13), and the target soil data is extracted based on the correlation wavelength set (Step 21 in FIG. 13). .. Then, the two-wavelength relational expression related to the selected correlation wavelength set is read out from the two-wavelength relational expression storage means 104 (Step 26 in FIG. 13), and the difference between the target soil data and the two-wavelength relational expression is calculated (Step 23 in FIG. 13).

When the difference between the target soil data and the two-wavelength relational expression is calculated, the difference is compared with the permissible range (Step 24 in FIG. 13). Then, if the difference is within the permissible range (Yes in Step 24 in FIG. 13), the water content ratio of the target soil is estimated based on the two-wavelength relational expression related to the selected correlation wavelength set (Step 22 in FIG. 13). .. On the other hand, when the difference is out of the permissible range (No in Step 24 in FIG. 13), the correlation wavelength set is reselected (Step 25 in FIG. 13), and a series of processes (Step 21, Step 26, Step 23, Step 24 in FIG. 13) are performed again. .. Alternatively, the range can be calculated for all the selected correlation wavelength sets, and the water content of the target soil can be estimated based on the correlation wavelength set with the smallest difference.

3. 3. Water content estimation program Subsequently, the water content estimation program of the present invention will be described with reference to FIG. The water content estimation program of the present invention causes a computer to execute some of the processes of the water content estimation device 100 described so far. Therefore, the description overlapping with the content described in the water content estimation device 100 is described. To avoid this, only the contents specific to the water content estimation program of the present invention will be described. That is, the contents not described here are the same as those described in "2. Water content estimation device".

FIG. 14 is a flow chart showing a main processing flow of the water content estimation program of the present invention. First, the wavelength set (first wavelength and second wavelength) input by the operator is accepted (Step 101 in FIG. 14). In addition, instead of (or in addition to) the specifications input by the operator, the wavelength sets can be automatically set in sequence. When the wavelength set is input (automatically set), the spectral patterns of a plurality of sample soils are read out and the sample soil data is extracted (Step 102 in FIG. 14).

When the sample soil data is extracted, the correlation coefficient is calculated from the sample soil data (Step 103 in FIG. 14), and the correlation coefficient is compared with the correlation threshold value (Step 104 in FIG. 14). Then, when the correlation coefficient falls below the correlation threshold value (No in Step 104 in FIG. 14), the wavelength set is input again (Step 101 in FIG. 14), and a series of processes (Step 102 to Step 104 in FIG. 14) are performed again. On the other hand, when the correlation coefficient exceeds the correlation threshold value (Yes in Step 104 in FIG. 14), the wavelength set is set as the “correlation wavelength set” and the process proceeds to the next step (Step 105 in FIG. 14). When the correlated wavelength set is selected, a two-wavelength relational expression is calculated (Step 105 in FIG. 14).

When the two-wavelength relational expression is prepared and the spectrum data of the target soil is obtained, the spectrum data of the target soil and the correlation wavelength set (correlation first wavelength and correlation second wavelength) are read out, and the target soil data is extracted (Fig.). Step 106 of 14). Then, the two-wavelength relational expression and the target soil data are read out to estimate the water content ratio of the target soil (Step 107 in FIG. 14). The estimated water content is output to an output means such as a printer or a display. When estimating the water content ratio, as shown in FIG. 11, the specifications can be used to estimate the water content after comparing the target soil data with the difference between the two wavelength relational expressions (regression line and regression curve) and the permissible range. Alternatively, as shown in FIG. 13, the specifications may be such that the most suitable correlation wavelength set (that is, a two-wavelength relational expression) is selected and then estimated.

4. Water content estimation method Subsequently, the water content estimation method of the present invention will be described with reference to FIG. The water content estimation method of the present invention is a method of estimating the water content state using the water content estimation device 100 described so far, and therefore, avoiding the description overlapping with the content explained by the water content estimation device 100, the present application. Only the contents peculiar to the method for estimating the water content state of the present invention will be described. That is, the contents not described here are the same as those described in "2. Water content estimation device".

FIG. 15 is a flow chart showing the flow of the main steps of the water content estimation method of the present invention. As shown in this figure, the water content estimation method of the present invention includes a "preliminary process" from photographing the sample soil to obtaining a two-wavelength relational expression, and actually photographing the target soil in the field to estimate the water content ratio. It can be roughly divided into the "observation process" up to. In this preliminary step, first, the multispectral data of the sample soil is acquired by photographing with a hyperspectral camera (Step 201 in FIG. 15). At this time, a plurality of types of sample soils having different water content are prepared, and multispectral data is acquired for each region RG in which each sample soil is divided.

When the multispectral data of the sample soil is acquired, the wavelength set (first wavelength and the second wavelength) is selected, the sample soil data is extracted, and the correlation coefficient is calculated (Step 202 in FIG. 15). Then, when the correlation coefficient exceeds the correlation threshold value, the two-wavelength relational expression is calculated (Step 203 in FIG. 15), and when the correlation coefficient falls below the correlation threshold value, the wavelength set is reselected.

When the two-wavelength relational expression is prepared by the preliminary process, a desired correlation wavelength set is selected for photographing the target soil. At this time, it is advisable to select the correlation wavelength set after predicting the soil quality of the target soil and the approximate water content ratio. Then, the observation spectrum camera is adjusted so that the measurement can be performed with the correlation first wavelength and the correlation second wavelength constituting the selected correlation wavelength set, and the target soil is actually photographed (Step 204 in FIG. 15). For example, when the multi-lens spectrum camera 210 shown in FIG. 2 is used, the filter 212 corresponding to the first correlation wavelength is attached to any of the five lenses 211, and the filter 212 corresponding to the second correlation wavelength is attached. .. In the case where a plurality of two-wavelength relational expressions are calculated, two or more correlated wavelength sets are selected, and the observation spectrum camera is adjusted so that the measurement can be performed with the two types of correlated first wavelength and correlated second wavelength. You can also take a picture of the target soil after doing so.

When the spectrum data of the target soil is obtained by taking a picture with an observation spectrum camera, the target soil data is extracted based on the spectrum data of the target soil and the correlation wavelength set, and the water content of the target soil is extracted from the two-wavelength relational expression and the target soil data. Estimate the ratio (Step 205 in FIG. 15). In estimating the water content ratio, as shown in FIG. 11, it is possible to estimate by comparing the target soil data with the difference between the two wavelength relational expressions (regression line and regression curve) and the permissible range, or in the figure. As shown in 13, the most suitable correlation wavelength set (that is, the two-wavelength relational expression) can be selected and estimated.

The water content estimation device, the water content estimation program, and the water content estimation method of the present invention can be used for the removed soil buried in the intermediate storage facility, as well as the embankment, road bed and road body, river embankment, coastal embankment, and dam. , Can be widely used for soil structures such as dams. The invention of the present application is an invention that can be used not only industrially but also can be expected to make a great contribution to society, considering that it provides a suitable solution for the management of contaminated soil.

100 Water content estimation device 101 (of water content estimation device) Correlation degree calculation means 102 (water content estimation device) Two-wavelength relational expression calculation process 103 (water content estimation device) Water content estimation means 104 (water content state estimation device) Two-wavelength relational storage means (of the estimator) 105 (of the water content estimator) Spectral data storage means 200 (of the water content estimator) Observation means 210 Multi-eye spectrum camera 211 (of the multi-lens spectrum camera) Lens 212 ( Filter RG region (of multi-lens spectrum camera)

Claims (7)

It is a device that estimates the water content of the target soil to be measured based on the results obtained by photographing the sample soil with known water content such as water content ratio and water content with a hyperspectral camera.
From the spectral data obtained from the plurality of types of sample soils having different water content, the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength different from the first wavelength are extracted, and at the same time, The first spectral intensity and the second spectral intensity obtained from the same water-containing state are used as a set of sample soil data, and the first spectral intensity and the second spectral intensity are obtained from the sample soil data related to a plurality of water-containing states. Correlation degree calculation means for determining the degree of correlation of
When the degree of correlation exceeds a predetermined correlation threshold value, a two-wavelength relational expression calculation process for obtaining a two-wavelength relational expression representing the relationship between the first spectral intensity and the second spectral intensity, and
Based on the target soil data consisting of the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength obtained by photographing the target soil with a spectrum camera, and the two-wavelength relational expression. A means for estimating the water content of the target soil, and a means for estimating the water content of the target soil.
A water content estimation device. An observation means having an unmanned vehicle, a multi-lens spectrum camera mounted on the unmanned vehicle, and a control means for controlling the unmanned vehicle and the multi-lens spectrum camera is further provided.
The multi-lens spectrum camera is provided with two or more lenses, and each lens is provided with a filter for acquiring spectral intensity at a desired wavelength.
The water content estimation means is based on the target soil data obtained by photographing the target soil with the multi-lens spectrum camera while the unmanned vehicle is flying, and the two-wavelength relational expression. Estimate the water content of the soil,
The water content estimation device according to claim 1. The water content estimation means estimates the water content of the target soil when the difference between the target soil data and the two-wavelength relational expression is within a predetermined allowable range.
The water content estimation device according to claim 1 or 2, wherein the water content estimation device is characterized. Further comprising a two-wavelength relational expression storage means for storing two or more said two-wavelength relational expressions composed of a combination of two different wavelengths
The water-containing state estimation means selects the first spectral intensity and the second spectral intensity from the spectral intensities of three or more wavelengths obtained by photographing the target soil with a spectrum camera, and the first spectrum. The two-wavelength relational expression relating to the intensity and the second spectral intensity is read out from the two-frequency relational expression storage means, and the two-wavelength relational expression read out, and the target soil data based on the first spectral intensity and the second spectral intensity. And, when the difference between the target soil data and the two-wavelength relational expression is out of the predetermined allowable range, the other first spectral intensity and the second spectral intensity are selected.
When the difference between the read-out two-wavelength relational expression and the first spectral intensity and the second spectral intensity related to the two-wavelength relational expression is within the permissible range, the water content estimation means of the target soil Estimate the water content,
The water content estimation device according to claim 1 or 2, wherein the water content estimation device is characterized. It is a program that causes a computer to execute a function to estimate the water content of the target soil to be measured based on the results obtained by photographing the sample soil with known water content such as water content ratio and water content with a hyperspectral camera. hand,
From the spectral data obtained from the plurality of types of sample soils having different water content, the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength different from the first wavelength are extracted, and at the same time, The first spectral intensity and the second spectral intensity obtained from the same water-containing state are used as a set of sample soil data, and the first spectral intensity and the second spectral intensity are obtained from the sample soil data related to a plurality of water-containing states. Correlation degree calculation processing to find the degree of correlation of
When the degree of correlation exceeds a predetermined correlation threshold value, a two-wavelength relational expression calculation process for obtaining a two-wavelength relational expression representing the relationship between the first spectral intensity and the second spectral intensity, and
Based on the target soil data consisting of the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength obtained by photographing the target soil with a spectrum camera, and the two-wavelength relational expression. It has a function of causing the computer to execute a water content estimation process for estimating the water content of the target soil.
A water content estimation program characterized by this. It is a method of estimating the water content of the target soil to be measured based on the results obtained by photographing the sample soil whose water content such as the water content ratio and the water content is known with a hyperspectral camera.
A sample soil photographing process in which a plurality of types of sample soils having different water content states are photographed with a hyperspectral camera and spectral data is acquired for each water content state.
Two different types of wavelengths are selected as the first wavelength and the second wavelength, and among the spectral data acquired for each water-containing state, the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength. The first spectral intensity and the second spectral intensity related to the same water-containing state are used as a set of sample soil data, and the first spectral intensity and the second spectral intensity and the second spectral intensity related to a plurality of water-containing states are used as a set of sample soil data. Correlation degree calculation process to find the degree of correlation of spectral intensity,
When the degree of correlation exceeds a predetermined correlation threshold value, a two-wavelength relational expression calculation step for obtaining a two-wavelength relational expression representing the relationship between the first spectral intensity and the second spectral intensity, and a step of calculating the two-wavelength relational expression.
A target soil photographing step of photographing the target soil with a spectrum camera to acquire the first spectral intensity at the first wavelength and the second spectral intensity at the second wavelength.
A water content estimation step for estimating the water content state of the target soil based on the target soil data consisting of the first spectral intensity and the second spectral intensity acquired in the target soil photographing step and the two wavelength relational expression. With,
A method for estimating the water content state. Using an observation means having an unmanned vehicle, a multi-lens spectrum camera mounted on the unmanned vehicle, and a control means for controlling the unmanned vehicle and the multi-lens spectrum camera.
The multi-lens spectrum camera is provided with two or more lenses, and each lens is provided with a filter for acquiring spectral intensity at a desired wavelength.
In the target soil photographing step, after attaching a filter to the lens so as to photograph at the first wavelength and the second wavelength according to the two-wavelength relational expression, the multi-lens spectrum camera while the unmanned flying object is flying. The target soil is photographed by
The water content estimation method according to claim 6, wherein the water content state is estimated.

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