Data acquisition device, data acquisition method, and data acquisition program

Abstract

To provide a data acquisition apparatus, a data acquisition method, and a data acquisition program.SOLUTION: In a remote sensing system 1 in which databases 10a, 10b and a data acquisition apparatus 100 are interconnected via a network 20, the data acquisition apparatus 100 comprises: a first image data acquisition unit for acquiring first image data indicating a first image obtained by photographing a target area from a satellite; a calculation unit for calculating a vegetation index in the target area using the first image data; a determination unit for determining whether or not the vegetation index satisfies predetermined evaluation criteria; and a second image data acquisition unit for acquiring second image data indicating a second image obtained by photographing the target area at a higher resolution than the first image when the vegetation index does not satisfy the evaluation criteria.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a data acquisition device, a data acquisition method, and a data acquisition program.

Background Art

[0002] Patent Document 1 describes that "it is desirable to perform imaging at a time when there are many fields at the expected growth stage arrival time." [Prior Art Document] [Patent Document] [Patent Document 1] JP-A-2021-006017 [Patent Document 2] JP-A-2000-194833 [Patent Document 3] WO2019 / 532380

Summary of the Invention

[0003] In a first aspect of the present invention, a data acquisition device is provided. The data acquisition device includes a first image data acquisition unit that acquires first image data indicating a first image obtained by photographing a target area from a satellite, a calculation unit that calculates a vegetation index in the target area using the first image data, a determination unit that determines whether the vegetation index satisfies a predetermined evaluation criterion, and a second image data acquisition unit that acquires second image data indicating a second image obtained by photographing the target area with a higher resolution than the first image when the vegetation index does not satisfy the evaluation criterion.

[0004] In the data acquisition device, the first image data acquisition unit may acquire a plurality of first image data indicating a plurality of first images obtained by photographing the target area at a plurality of time points, the calculation unit may calculate the vegetation indices at the plurality of time points using the plurality of first image data, and the determination unit may determine whether a statistic of the vegetation indices at the plurality of time points satisfies the evaluation criterion.

[0005] In any of the data acquisition devices, the statistic of the vegetation index may be the maximum value of the vegetation indices at the plurality of time points.

[0006] Any one of the data acquisition devices may further include a data output unit that outputs the second image data.

[0007] Any one of the data acquisition devices may further include a data analysis unit that analyzes the second image data and an analysis result output unit that outputs the analyzed result.

[0008] Any one of the data acquisition devices may further include a reference setting unit that sets the evaluation criteria based on the performance of the vegetation index.

[0009] Any one of the data acquisition devices may further include an environmental data acquisition unit that acquires environmental data indicating the shooting environment in which the first image was shot, and the reference setting unit may set the evaluation criteria using the environmental data.

[0010] In any one of the data acquisition devices, the reference setting unit may set the evaluation criteria using a learning model that is machine-learned to output the evaluation criteria in response to input of the environmental data.

[0011] Any one of the data acquisition devices may further include a specifying unit that specifies the resolution of the second image according to the vegetation index.

[0012] In any one of the data acquisition devices, the specifying unit may specify the resolution of the second image based on the difference between the vegetation index and the evaluation criteria.

[0013] In a second aspect of the present invention, a data acquisition method is provided. The data acquisition method is executed by a computer, and the computer acquires first image data indicating a first image obtained by photographing a target area from a satellite, calculates a vegetation index in the target area using the first image data, determines whether the vegetation index meets a predetermined evaluation criterion, and when the vegetation index does not meet the evaluation criterion, acquires second image data indicating a second image obtained by photographing the target area with a higher resolution than the first image.

[0014] In a third aspect of the present invention, a data acquisition program is provided. The data acquisition program is executed by a computer, and causes the computer to function as a first image data acquisition unit that acquires first image data indicating a first image obtained by photographing a target area from a satellite, a calculation unit that calculates a vegetation index in the target area using the first image data, a determination unit that determines whether the vegetation index meets a predetermined evaluation criterion, and a second image data acquisition unit that acquires second image data indicating a second image obtained by photographing the target area with a higher resolution than the first image when the vegetation index does not meet the evaluation criterion.

[0015] Note that the above summary of the invention does not list all the features of the present invention. Also, sub-combinations of these feature groups can also be inventions.

Brief Description of the Drawings

[0016]

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Embodiments for Carrying Out the Invention

[0017] Hereinafter, the present invention will be described through embodiments of the invention. However, the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution means of the invention.

[0018] FIG. 1 shows an example of a block diagram of a remote sensing system 1 that may include a data acquisition device 100 according to a first embodiment. Note that these blocks are functionally separated functional blocks and do not necessarily match the actual device configuration. That is, in this figure, just because something is shown as one block does not mean it must be constituted by one device. Also, in this figure, just because things are shown as separate blocks does not mean they must be constituted by separate devices. The same applies to the block diagrams hereinafter.

[0019] In remote sensing, attempts have been made to monitor vegetation in a target area using image data obtained by photographing the target area. In this case, in order to monitor vegetation in detail, it is preferable to use image data with high resolution. However, increasing the resolution may lead to an increase in the purchase cost and communication cost of the image data. In addition, as the resolution is increased, the shooting range per image becomes narrower, and in order to monitor a wide area, it may be necessary to acquire and synthesize a plurality of image data, resulting in complicated processing. Therefore, it may not be realistic to frequently acquire high-resolution image data. Thus, the data acquisition device 100 triggers the acquisition of relatively high-resolution image data using relatively low-resolution image data in the remote sensing system 1. The remote sensing system 1 includes a database 10, a network 20, and a data acquisition device 100.

[0020] The database 10 stores various image data indicating images taken of various areas at various times. In addition to the image data, the database 10 may store various other data useful for acquiring the image data. In this figure, an example is shown in which the database 10 includes a first database 10a and a second database 10b (collectively referred to as the "database 10").

[0021] The first database 10a stores satellite image data indicating satellite images taken from a satellite. Such a satellite may be an artificial satellite that observes the Earth using radio waves, infrared rays, and visible light, and is also called an Earth observation satellite or a remote sensing satellite. For example, when the satellite receives a command to take a satellite image, it may take a satellite image according to the command. Next, the ground station may receive satellite image data indicating the taken satellite image from the satellite and supply it to the processing station. Then, the processing station may perform various processes (cropping process, radiometric correction, geometric correction, etc.) on the satellite image data. The first database 10a may store, for example, the satellite image data thus processed in association with metadata such as the date and time of shooting and the processing content. Here, the satellite image may include an area that at least partially overlaps with another satellite image and is taken with a higher resolution than the other satellite image.

[0022] Hereinafter, a case where the first database 10a stores visible and reflected infrared remote sensing data will be described as an example. Therefore, the satellite image data stored in the first database 10a may be data of a multispectral image.

[0023] Generally, various objects (water, soil, plants, etc.) that make up the earth's surface have different reflectance characteristics for each spectrum depending on the type. For example, "water" has a main reflection region in the visible range (0.4 to 0.7 μm). "Soil" tends to have stronger reflection as the wavelength increases, and has a main reflection region in the short-wavelength infrared range (1.3 to 3 μm). "Plants" have the characteristic of efficiently absorbing the visible range by the action of photosynthetic pigments and reflecting the near-infrared range (0.7 to 1.3 μm). It can be said that the data of a multispectral image that records electromagnetic waves of different spectra is important data for grasping the spectral reflection characteristics of various objects that make up the earth's surface.

[0024] However, it is not limited to this. The first database 10a can also store other remote sensing data such as thermal infrared remote sensing data and microwave remote sensing data.

[0025] The second database 10b stores aerial image data showing aerial images taken from aircraft, drones, etc. Various processes may be performed on such aerial image data. The second database 10b may store, for example, the aerial image data subjected to such processes in association with metadata such as the date and time of shooting and the content of the process. Here, the aerial image may be an image taken with a higher resolution than the satellite image of an area that at least partially overlaps with the satellite image.

[0026] The aerial image data stored in the second database 10b may also be multi-spectral image data. However, it is not limited to this. The second database 10b can also store other remote sensing data, similar to the first database 10a. Also, the satellite image data stored in the first database 10a and the aerial image data stored in the second database 10b do not necessarily have to be of the same image type, and they may be of different image types.

[0027] The user can obtain or purchase the satellite image data stored in the first database 10a and the aerial image data stored in the second database 10b, for example, through satellite operating agencies or agents.

[0028] Network 20 is a network that connects multiple computers. For example, network 20 may be a global network that interconnects multiple computer networks. As an example, network 20 may be the Internet that uses the Internet Protocol. Alternatively, network 20 may be realized by a dedicated line. Network 20 interconnects various devices in remote sensing system 1, in this figure, database 10 and data acquisition device 100.

[0029] Data acquisition device 100 may be a computer such as a PC (personal computer), a tablet computer, a smartphone, a workstation, a server computer, or a general-purpose computer, or may be a computer system to which a plurality of computers are connected. Such a computer system is also a computer in a broad sense. Further, data acquisition device 100 may be implemented by one or more executable virtual computer environments within a computer. Alternatively, data acquisition device 100 may be a dedicated computer designed for data acquisition, or may be dedicated hardware realized by a dedicated circuit. Also, when connectable to the Internet, data acquisition device 100 may be realized by cloud computing.

[0030] Data acquisition device 100 monitors vegetation in a target area using relatively low-resolution image data, and acquires relatively high-resolution image data triggered by the vegetation index not meeting the evaluation criteria. Data acquisition device 100 includes a first image data acquisition unit 110, a calculation unit 120, a determination unit 130, a second image data acquisition unit 140, and a data output unit 150.

[0031] The first image data acquisition unit 110 acquires first image data indicating a first image obtained by photographing a target area from a satellite. Here, the target area is an area of interest (AOI) that a user desires to monitor vegetation. The first image data acquisition unit 110 supplies the acquired first image data to the calculation unit 120.

[0032] The calculation unit 120 calculates a vegetation index in the target area using the first image data. Here, the vegetation index is an index indicating the amount and activity level of plants. The calculation unit 120 supplies the calculated vegetation index to the determination unit 130.

[0033] The determination unit 130 determines whether the vegetation index satisfies a predetermined evaluation criterion. The determination unit 130 supplies the determined result to the second image data acquisition unit 140.

[0034] When the vegetation index does not satisfy the evaluation criterion, the second image data acquisition unit 140 acquires second image data indicating a second image obtained by photographing the target area with a higher resolution than the first image. The second image data acquisition unit 140 supplies the acquired second image data to the data output unit 150.

[0035] The data output unit 150 outputs the second image data. A method for acquiring high-image data by the data acquisition device 100 having such functional units will be described in detail using a flowchart.

[0036] FIG. 2 shows an example of a flowchart of a data acquisition method by the data acquisition device 100 according to the first embodiment.

[0037] In step S210, the data acquisition device 100 acquires first image data. For example, the first image data acquisition unit 110 may acquire first image data indicating a first image obtained by photographing a target area from a satellite. As an example, the first image data acquisition unit 110 may receive a specification of the first image. Accordingly, for example, it is assumed that the user specifies the target area by various types of information such as an address, a place name, a target object, a postal code, etc., or by geographical coordinates (for example, latitude and longitude) assigned to the various types of information by geocoding. Also, it is assumed that the user specifies the shooting timing by the shooting date and time. In this case, the first image data acquisition unit 110 may search the first database 10a using the specified target area and shooting timing as search keys. Then, the first image data acquisition unit 110 may acquire, as the first image data, the satellite image data retrieved from the first database 10a via the network 20.

[0038] However, it is not limited to this. The first image data acquisition unit 110 may acquire the first image data via various memory devices or user input, or may acquire the first image data from another device different from the first database 10a. Also, when the specified image is not archived, the first image data acquisition unit 110 may transmit a command for instructing the satellite to take a picture via a ground station, and acquire, as the first image data, the data of the satellite image taken in response to the command.

[0039] Here, it is preferable that the first image is a plurality of images obtained by photographing the target area from a satellite at different points in time. Therefore, the first image data acquisition unit 110 may acquire a plurality of first image data indicating a plurality of first images obtained by photographing the target area at a plurality of points in time. As an example, the first image data acquisition unit 110 may acquire a plurality of first image data indicating a plurality of first images obtained by periodically (for example, every day) photographing the target area over a predetermined period (for example, one week). The first image data acquisition unit 110 supplies the first image data thus acquired to the calculation unit 120, for example.

[0040] In step S220, the data acquisition device 100 calculates a vegetation index. For example, the calculation unit 120 may calculate the vegetation index in the target area using the first image data acquired in step S210. As an example, the calculation unit 120 may calculate the vegetation index by performing an inter-band operation on information in different wavelength bands of the first image, which is a multispectral image.

[0041] Here, in vegetation monitoring by remote sensing, an index called the Normalized Difference Vegetation Index (NDVI) that utilizes the spectral reflectance characteristics of plants is widely used.

[0042] NDVI is calculated by the mathematical formula "NDVI = (NIR - RED) / (NIR + RED)". Here, NIR represents the reflectance in the near-infrared region, and RED represents the reflectance in the visible red region. That is, NDVI is the result of dividing the difference between the reflectances of the near-infrared band and the red band reflected by plants by their sum. NDVI is represented by a numerical value normalized to a value between -1 and 1, and the denser the vegetation, the larger the value. The calculation unit 120 may calculate the vegetation index in the target area using such NDVI, for example.

[0043] Note that in the above description, the case of using NDVI as the vegetation index is shown as an example, but it is not limited thereto. Instead of or in addition to NDVI, the calculation unit 120 may calculate the vegetation index in the target area using other indices such as the Extended Vegetation Index (EVI) or the Leaf Area Index (LAI).

[0044] Here, when a plurality of first image data are acquired in step S210, the calculation unit 120 may calculate the vegetation indices at a plurality of time points using the plurality of first image data. As an example, the calculation unit 120 may calculate the NDVI for each day over a one-week period. The calculation unit 120 supplies the calculated vegetation indices to the determination unit 130.

[0045] In step S230, the data acquisition device 100 determines whether the vegetation index meets the evaluation criteria. For example, the determination unit 130 may determine whether the vegetation index calculated in step S230 meets a predetermined evaluation criterion. As an example, the determination unit 130 may determine whether the calculated NDVI is equal to or greater than a predetermined threshold value. And when the NDVI is equal to or greater than the threshold value, the determination unit 130 may determine that the vegetation index meets the evaluation criteria. Also, when the NDVI is less than the threshold value, the determination unit 130 may determine that the vegetation index does not meet the evaluation criteria. Note that the evaluation criteria (such as the threshold value) used at this time may be a fixed value specified in advance by the user, or may be a variable value that varies according to various shooting environments. This will be described later.

[0046] Here, when the vegetation indices at a plurality of time points are calculated in step S220, the determination unit 130 may determine whether the statistic of the vegetation indices at the plurality of time points meets the evaluation criteria. Generally, NDVI takes a lower value when the ground surface is covered by clouds than when it is not covered by clouds. Therefore, if the maximum NDVI value during a period in which the amount and activity of plants can be regarded as constant is taken as the representative value of the period, the influence of clouds can be minimized. Therefore, the statistic of the vegetation index is preferably the maximum value of the vegetation indices at a plurality of time points.

[0047] However, it is not limited to this. As the statistic, instead of or in addition to the maximum value, other statistical values such as an average value or a median value may be used. The determination unit 130 supplies the determined result to the second image data acquisition unit 140.

[0048] As a result of the determination, when the vegetation index meets the evaluation criteria (Yes), the data acquisition device 100 proceeds to step S260. That is, the data acquisition device 100 omits steps S240 and S250. On the other hand, when the vegetation index does not meet the evaluation criteria (No), the data acquisition device 100 proceeds to step S240.

[0049] In step S240, the data acquisition device 100 acquires second image data. For example, when the vegetation index does not meet the evaluation criteria, the second image data acquisition unit 140 may acquire second image data indicating a second image obtained by photographing the target area at a higher resolution than the first image. As an example, the second image data acquisition unit 140 may specify the timing closest to the imaging timing of the first image as the imaging timing of the second image. When the first image is a plurality of images taken at a plurality of time points, the second image data acquisition unit 140 may also specify the timing closest to the imaging timing of the latest first image as the imaging timing of the second image. Next, the second image data acquisition unit 140 may search the second database 10b using the specified target area and imaging timing as search keys. Then, the second image data acquisition unit 140 may acquire, via the network 20, the aerial image data retrieved from the second database 10b as the second image data.

[0050] However, it is not limited thereto. The second image data acquisition unit 140 may acquire the second image data via various memory devices or user input, or may acquire the second image data from another device different from the second database 10b. In particular, the second image data acquisition unit 140 can also acquire the second image data from the first database 10a. In this case, the second image data acquisition unit 140 may search the first database 10a only for satellite images with a higher resolution than the resolution of the first image. Also, when the specified image is not archived, the second image data acquisition unit 140 may send a command to a command to an aircraft, a drone, etc., or a satellite to take a picture, and acquire, as the second image data, the data of the aerial image or satellite image taken in response to the command. The second image data acquisition unit 140 supplies the acquired second image data to the data output unit 150.

[0051] In step S250, the data acquisition device 100 outputs the second image data. For example, the data output unit 150 may output the second image data obtained in step S240 by displaying it on a monitor. However, it is not limited to this. The data output unit 150 may output the second image data by printing it out, or may output it by transmitting it to another device.

[0052] In step S260, the data acquisition device 100 determines whether to end the monitoring. And as a result of the determination, if the monitoring is not ended (in the case of No), the data acquisition device 100 returns the process to step S210 and continues the flow. On the other hand, as a result of the determination, if the monitoring is to be ended (in the case of Yes), the data acquisition device 100 ends the flow. At this time, the data acquisition device 100 may determine whether to end the monitoring according to a predetermined rule (such as a time trigger or an event trigger), or may determine whether to end the monitoring according to an instruction from the user.

[0053] In remote sensing, it is preferable to use high-resolution image data to monitor vegetation in detail. However, increasing the resolution can lead to an increase in cost and complexity of processing. Conventionally, based on meteorological data, the growth status of crops was predicted, and the timing when the crops reached a specific growth stage was determined as the appropriate timing for taking remote sensing images. However, in this case, even if vegetation degradation or artificial deforestation occurred due to climate change, it was difficult to analyze these causes.

[0054] In contrast, the data acquisition device 100 monitors the vegetation in the target area using relatively low-resolution image data, and acquires relatively high-resolution image data triggered by the vegetation index not meeting the evaluation criteria. Thereby, according to the data acquisition device 100, it is possible to monitor the vegetation using low-resolution image data and acquire high-resolution image data only when vegetation deterioration, forest destruction, etc. occur. Therefore, according to the data acquisition device 100, usually while suppressing the acquisition frequency of high-resolution image data, when any abnormality occurs in the vegetation, it is possible to acquire image data with sufficient resolution for analyzing the cause.

[0055] Further, the data acquisition device 100 may further include a function of outputting the high-resolution image data acquired in this way. Thereby, the data acquisition device 100 can provide the user with information for analyzing the cause of the abnormality in the vegetation and contribute to early clarification of the cause.

[0056] FIG. 3 shows an example of a block diagram of a remote sensing system 1 that may include the data acquisition device 100 according to the second embodiment. In FIG. 3, members having the same functions and configurations as those in FIG. 1 are denoted by the same reference numerals, and the description is omitted except for the following differences. In the above-described embodiment, the case where the data acquisition device 100 outputs the acquired second image data as it is was described as an example. However, in the second embodiment, the data acquisition device 100 analyzes the acquired second image data and outputs the result. The data acquisition device 100 according to the second embodiment further includes, instead of or in addition to the data output unit 150, a data analysis unit 310 and an analysis result output unit 320. Further, in the data acquisition device 100 according to the second embodiment, the second image data acquisition unit 140 supplies the acquired second image data to the data analysis unit 310.

[0057] The data analysis unit 310 analyzes the second image data acquired by the second image data acquisition unit 140. For example, the data analysis unit 310 may analyze the second image data to generate an NDVI image. Also, if there is a history of analyzing the second image data in the past, the data analysis unit 310 may detect changes in vegetation in the target area based on the difference between the current NDVI image and the past NDVI image. The data analysis unit 310 supplies the results analyzed in this way to the analysis result output unit 320, for example.

[0058] The analysis result output unit 320 outputs the results analyzed by the data analysis unit 310. For example, the analysis result output unit 320 may output by displaying the NDVI image on a monitor. However, it is not limited to this. The analysis result output unit 320 may also output the analysis results by printing them out, or may output them by transmitting them to another device. Also, when the change in vegetation exceeds a predetermined standard, the analysis result output unit 320 may generate an alert to that effect.

[0059] In this way, the data acquisition device 100 according to the second embodiment may further include a function of analyzing the second image data in addition to the function of acquiring the second image data. Thereby, according to the data acquisition device 100 according to the second embodiment, the function of acquiring high-resolution image data and the function of analyzing the high-resolution image data can be realized by one device.

[0060] Also, the data acquisition device 100 according to the second embodiment may further include a function of outputting the analyzed results. Thereby, according to the data acquisition device 100 according to the second embodiment, a highly accurate analysis result using high-resolution image data can be provided to the user.

[0061] FIG. 4 shows an example of a block diagram of a remote sensing system 1 that may include a data acquisition device 100 according to a third embodiment. In FIG. 4, members having the same functions and configurations as those in FIG. 1 are denoted by the same reference numerals, and the description thereof will be omitted except for the following differences. In the above-described embodiment, the case where the data acquisition device 100 uses a fixed value predetermined as an evaluation criterion for determining a vegetation index has been described as an example. However, in the third embodiment, the data acquisition device 100 uses a variable value that varies according to various shooting environments as an evaluation criterion. In the third embodiment, the database 10 further includes a third database 10c. Further, the data acquisition device 100 according to the third embodiment further includes an environmental data acquisition unit 410 and a reference setting unit 420. Further, in the data acquisition device 100 according to the third embodiment, the calculation unit 120 supplies the calculated vegetation index to the reference setting unit 420 in addition to the determination unit 130.

[0062] The third database 10c stores environmental data indicating various shooting environments in various areas. Such environmental data may include, for example, meteorological data published by the Japan Meteorological Agency, private meteorological service support centers, and meteorological operators (referred to as "the Japan Meteorological Agency, etc."). However, it is not limited thereto. The environmental data may include various data that can affect the shooting of satellite images, such as data indicating the distribution of vegetation (for example, the distribution of deciduous forests and the distribution of evergreen forests).

[0063] The environmental data acquisition unit 410 acquires environmental data indicating the shooting environment in which the first image was taken. For example, the environmental data acquisition unit 410 may search the third database 10c using the target area of the first image, the shooting timing, and the type of environmental data as search keys. Here, examples of the type of environmental data include weather, cloud cover, and vegetation distribution. Then, the environmental data acquisition unit 410 may download the retrieved data via the network 20 as, for example, a CSV (Comma Separated Value) file. The environmental data acquisition unit 410 may acquire environmental data in this way, for example. The environmental data acquisition unit 410 supplies the acquired environmental data to the reference setting unit 420.

[0064] The reference setting unit 420 sets evaluation criteria based on the actual performance of the vegetation index. For example, the reference setting unit 420 may statistically process the vegetation indices calculated by the calculation unit 120 in the past and set the evaluation criteria according to the statistical results. At this time, the reference setting unit 420 may also set the evaluation criteria using the environmental data acquired by the environmental data acquisition unit 410. That is, the reference setting unit 420 may set a variable value that varies according to various shooting environments as the evaluation criteria. As an example, when the relationship between each type of environmental data and the recommended evaluation criteria is known in advance from the actual performance of the vegetation index, the reference setting unit 420 may set, as the evaluation criteria, the result calculated according to the input of the environmental data into a predefined function.

[0065] On the other hand, when the relationship between each type of environmental data and the recommended evaluation criteria is unknown, the reference setting unit 420 may use a machine learning model. For example, the reference setting unit 420 may define the model as f(E, W)=O, where E is the environmental data, W is the weight, and O is the model output. Then, during the learning period, the reference setting unit 420 may update the weight W so that the model output O output according to the input of the environmental data E into the model approaches the actual value of the vegetation index. Then, during the operation period, the reference setting unit 420 may set the evaluation criteria using the learning model that has been machine-learned to output the evaluation criteria according to the input of the environmental data in this way.

[0066] The reference setting unit 420 supplies the evaluation criteria set in this way to the determination unit 130, for example. In response to this, the determination unit 130 may determine whether the vegetation index calculated by the calculation unit 120 satisfies the evaluation criteria set by the reference setting unit 420.

[0067] In this way, the data acquisition device 100 according to the third embodiment may set the evaluation criteria based on the actual results of the vegetation index. Thus, according to the data acquisition device 100 according to the third embodiment, instead of using a predetermined fixed value as the evaluation criteria for determining the vegetation index, a variable value considering the actual results of the vegetation index can be used.

[0068] Further, the data acquisition device 100 according to the third embodiment may acquire environmental data indicating the shooting environment in which the first image is shot, and set the evaluation criteria using the environmental data. As described above, vegetation indices such as NDVI take lower values when the ground surface is covered by clouds than when it is not covered by clouds. Also, vegetation indices such as NDVI show seasonal changes such as a decrease in value in winter due to leaf fall and an increase in value in summer in areas with many deciduous forests, while the seasonal changes are small in areas with many evergreen forests. Therefore, the data acquisition device 100 according to the third embodiment may set a variable value that varies according to such a shooting environment as the evaluation criteria. Thus, according to the data acquisition device 100 according to the third embodiment, the vegetation index can be determined using optimal evaluation criteria considering various shooting environments.

[0069] Further, the data acquisition device 100 according to the third embodiment can also set the evaluation criteria using a machine learning model. Thus, according to the data acquisition device 100 according to the third embodiment, even when the relationship between various types of environmental data and the recommended evaluation criteria is unknown, an optimal evaluation criteria can be set.

[0070] FIG. 5 shows an example of a block diagram of a remote sensing system 1 that may include a data acquisition device 100 according to a fourth embodiment. In FIG. 5, members having the same functions and configurations as those in FIG. 1 are denoted by the same reference numerals, and the description thereof will be omitted except for the following differences. In the above-described embodiment, as an example, a case where the resolution of the second image may be any resolution higher than the resolution of the first image has been described. However, in the fourth embodiment, the data acquisition device 100 designates the resolution of the second image. The data acquisition device 100 according to the fourth embodiment further includes a designation unit 510. Further, in the data acquisition device 100 according to the fourth embodiment, the calculation unit 120 supplies the calculated vegetation index to the designation unit 510 in addition to the determination unit 130. Further, in the data acquisition device 100 according to the fourth embodiment, the determination unit 130 supplies the determined result to the designation unit 510 instead of the second image data acquisition unit 140.

[0071] The designation unit 510 designates the resolution of the second image according to the vegetation index. For example, when it is determined in the determination unit 130 that the vegetation index calculated by the calculation unit 120 does not satisfy the evaluation criteria, the designation unit 510 may designate the resolution of the second image according to the vegetation index. Here, when the difference between the vegetation index and the evaluation criteria is large, there is a possibility that the degree of abnormality in the vegetation is high, and it is considered that high-precision analysis is required. Therefore, the designation unit 510 may designate the resolution of the second image so that the resolution becomes higher as the difference between the vegetation index and the evaluation criteria becomes larger. The designation unit 510 can designate the resolution of the second image based on the difference between the vegetation index and the evaluation criteria in this way, for example.

[0072] At this time, the designation unit 510 may directly designate the resolution of the second image, or may designate an allowable range (for example, a lower limit value, etc.) of the resolution of the second image. The designation unit 510 notifies the designated resolution to the second image data acquisition unit 140. In response to this, the second image data acquisition unit 140 may search for the second image at the designated resolution and acquire the second image data.

[0073] Thus, the data acquisition device 100 according to the fourth embodiment may specify the resolution of the second image according to the vegetation index. Thereby, according to the data acquisition device 100 according to the fourth embodiment, the second image data can be acquired at an optimal resolution considering the vegetation index.

[0074] Further, the data acquisition device 100 according to the fourth embodiment may specify the resolution of the second image based on the difference between the vegetation index and the evaluation criterion. Thereby, according to the data acquisition device 100 according to the fourth embodiment, when the gap between the vegetation index and the evaluation criterion is large due to a high degree of abnormality in vegetation or the like, the second image data can be acquired at a higher resolution.

[0075] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of a device having the role of performing the operations. The specific stages and sections may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable medium. The dedicated circuit may include digital and / or analog hardware circuits, including integrated circuits (ICs) and / or discrete circuits. The programmable circuit may include a reconfigurable hardware circuit including memory elements such as logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), etc.

[0076] A computer-readable medium may include any tangible device that can store instructions executable by an appropriate device. As a result, a computer-readable medium having instructions stored therein will comprise a product that includes instructions that can be executed to create means for performing the operations specified in a flowchart or block diagram. Examples of computer-readable media may include electronic memory media, magnetic memory media, optical memory media, electromagnetic memory media, semiconductor memory media, and the like. More specific examples of computer-readable media may include floppy (registered trademark) disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, and the like.

[0077] Computer-readable instructions may include any combination of one or more programming languages, including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of object-oriented programming languages such as Smalltalk (registered trademark), JAVA (registered trademark), C++, and conventional procedural programming languages such as the "C" programming language or similar programming languages.

[0078] Computer-readable instructions may be provided to a processor or programmable circuitry of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, either locally or via a wide area network (WAN) such as a local area network (LAN), the Internet, etc., and may execute the computer-readable instructions to create means for performing the operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0079] FIG. 6 shows an example of a computer 9900 in which multiple aspects of the present invention may be embodied, in whole or in part. A program installed in the computer 9900 may cause the computer 9900 to function as an operation associated with an apparatus according to an embodiment of the present invention or as one or more sections of the apparatus, or to execute the operation or the one or more sections, and / or may cause the computer 9900 to execute a process according to an embodiment of the present invention or a stage of the process. Such a program may be executed by the CPU 9912 to cause the computer 9900 to perform certain operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0080] The computer 9900 according to this embodiment includes a CPU 9912, a RAM 9914, a graphic controller 9916, and a display device 9918, which are interconnected by a host controller 9910. The computer 9900 also includes an input / output unit such as a communication interface 9922, a hard disk drive 9924, a DVD drive 9926, and an IC card drive, which are connected to the host controller 9910 via an input / output controller 9920. The computer also includes legacy input / output units such as a ROM 9930 and a keyboard 9942, which are connected to the input / output controller 9920 via an input / output chip 9940.

[0081] The CPU 9912 operates according to programs stored in the ROM 9930 and the RAM 9914, thereby controlling each unit. The graphic controller 9916 acquires image data generated by the CPU 9912 in a frame buffer or the like provided in the RAM 9914 or in itself, and causes the image data to be displayed on the display device 9918.

[0082] The communication interface 9922 communicates with other electronic devices via a network. The hard disk drive 9924 stores programs and data used by the CPU 9912 in the computer 9900. The DVD drive 9926 reads a program or data from the DVD-ROM 9901 and provides the program or data to the hard disk drive 9924 via the RAM 9914. The IC card drive reads programs and data from an IC card and / or writes programs and data to the IC card.

[0083] ROM 9930 stores therein a boot program or the like executed by computer 9900 at activation and / or a program dependent on the hardware of computer 9900. Input / output chip 9940 may also connect various input / output units to input / output controller 9920 via a parallel port, a serial port, a keyboard port, a mouse port, or the like.

[0084] The program is provided by a computer-readable medium such as DVD-ROM 9901 or an IC card. The program is read from the computer-readable medium, installed in hard disk drive 9924, RAM 9914, or ROM 9930, which are also examples of computer-readable media, and executed by CPU 9912. The information processing described in these programs is read by computer 9900, resulting in cooperation between the programs and the various types of hardware resources described above. The apparatus or method may be configured by realizing the operation or processing of information according to the use of computer 9900.

[0085] For example, when communication is executed between computer 9900 and an external device, CPU 9912 may execute a communication program loaded in RAM 9914 and instruct communication interface 9922 to perform communication processing based on the processing described in the communication program. Communication interface 9922 reads transmission data stored in a transmission buffer processing area provided in a recording medium such as RAM 9914, hard disk drive 9924, DVD-ROM 9901, or an IC card under the control of CPU 9912, transmits the read transmission data to the network, or writes the received data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0086] Further, the CPU 9912 may cause all or necessary parts of files or databases stored in external recording media such as a hard disk drive 9924, a DVD drive 9926 (DVD-ROM 9901), an IC card, etc. to be read into the RAM 9914, and execute various types of processing on the data on the RAM 9914. The CPU 9912 then writes back the processed data to the external recording media.

[0087] Various types of information such as various types of programs, data, tables, and databases may be stored in the recording media and may undergo information processing. The CPU 9912 may perform various types of processing on the data read from the RAM 9914, including various types of operations, information processing, condition judgment, conditional branch, unconditional branch, information search / replacement, etc. described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 9914. Also, the CPU 9912 may search for information in files, databases, etc. within the recording media. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording media, the CPU 9912 searches for an entry that matches the condition where the attribute value of the first attribute is specified from among the plurality of entries, reads the attribute value of the second attribute stored in the entry, and thereby may obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0088] The programs or software modules described above may be stored in a computer-readable medium on or near the computer 9900. Also, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the program to the computer 9900 via the network.

[0089] As described above, the present invention has been described using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. It is clear from the description of the claims that forms with such changes or improvements can also be included in the technical scope of the present invention.

[0090] It should be noted that in the claims, the specification, and the drawings, the execution order of each process such as operations, procedures, steps, and stages in the apparatus, system, program, and method shown is not explicitly stated as "earlier" or "preceding" etc., and can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the specification, and the drawings, even if it is described using "first," "next," etc. for convenience, it does not mean that it is essential to implement in this order.

Description of Reference Numerals

[0091] 1 Remote sensing system 10 Database 10a First database 10b Second database 10c Third database 100 Data acquisition device 110 First image data acquisition unit 120 Calculation unit 130 Judgment unit 140 Second image data acquisition unit 150 Data output unit 310 Data analysis unit 320 Analysis result output unit 410 Environmental data acquisition unit 420 Reference setting unit 510 Designation unit 9900 Computer 9901 DVD-ROM 9910 Host controller 9912 CPU 9914 RAM 9916 Graphic Controller 9918 Display Device 9920 Input / Output Controller 9922 Communication Interface 9924 Hard Disk Drive 9926 DVD Drive 9930 ROM 9940 Input / Output Chip 9942 Keyboard

Claims

1. A first image data acquisition unit that acquires first image data showing a first image obtained by photographing a target area from a satellite; A calculation unit that calculates a vegetation index in the target area using the first image data; A determination unit that determines whether the vegetation index satisfies a predetermined evaluation criterion; A second image data acquisition unit that, when the vegetation index does not satisfy the evaluation criterion, acquires second image data showing a second image obtained by photographing the target area with a higher resolution than the first image; comprising: The first image data acquisition unit acquires a plurality of first image data showing a plurality of first images obtained by photographing the target area at a plurality of time points, The calculation unit calculates the vegetation index at the plurality of time points using the plurality of first image data, respectively, The determination unit determines whether a statistic of the vegetation index at the plurality of time points satisfies the evaluation criterion, a data acquisition device.

2. The data acquisition device according to claim 1, wherein the statistic of the vegetation index is a maximum value of the vegetation index at the plurality of time points.

3. The data acquisition device according to claim 1 or 2, further comprising a data output unit that outputs the second image data.

4. A data analysis unit that analyzes the second image data; The data acquisition device according to claim 1 or 2, further comprising an analysis result output unit that outputs the analyzed result.

5. The data acquisition device according to claim 1 or 2, further comprising a criterion setting unit that sets the evaluation criterion based on the performance of the vegetation index.

6. Further comprising an environmental data acquisition unit that acquires environmental data indicating a photographing environment in which the first image was taken, The data acquisition device according to claim 5, wherein the reference setting unit sets the evaluation criteria using the environmental data.

7. The data acquisition device according to claim 6, wherein the reference setting unit sets the evaluation criteria using a learning model that is machine-learned to output the evaluation criteria in response to input of the environmental data.

8. A first image data acquisition unit that acquires first image data showing a first image obtained by photographing a target area from a satellite; A calculation unit that calculates a vegetation index in the target area using the first image data; A determination unit that determines whether or not the vegetation index satisfies a predetermined evaluation criterion; A second image data acquisition unit that acquires second image data showing a second image obtained by photographing the target area at a higher resolution than the first image when the vegetation index does not satisfy the evaluation criterion; A designation unit that designates the resolution of the second image according to the vegetation index; A data acquisition device comprising:

9. The data acquisition device according to claim 8, wherein the designation unit designates the resolution of the second image based on a difference between the vegetation index and the evaluation criterion.

10. Executed by a computer, the computer Acquires first image data showing a first image obtained by photographing a target area from a satellite; Calculates a vegetation index in the target area using the first image data; Determines whether or not the vegetation index satisfies a predetermined evaluation criterion; Acquires second image data showing a second image obtained by photographing the target area at a higher resolution than the first image when the vegetation index does not satisfy the evaluation criterion; Comprising, Obtaining the first image data includes obtaining a plurality of first image data indicating a plurality of first images obtained by photographing the target area at a plurality of time points. Calculating the vegetation index includes calculating the vegetation index at the plurality of time points using the plurality of first image data. Determining whether the evaluation criteria are met includes determining whether a statistic of the vegetation index at the plurality of time points meets the evaluation criteria. A data acquisition method.

11. Executed by a computer, the computer Obtaining first image data indicating a first image obtained by photographing a target area from a satellite, Calculating a vegetation index in the target area using the first image data, Determining whether the vegetation index meets a predetermined evaluation criterion, Designating the resolution of a second image obtained by photographing the target area at a higher resolution than the first image according to the vegetation index, Obtaining second image data indicating the second image when the vegetation index does not meet the evaluation criteria, A data acquisition method comprising.

12. Executed by a computer, the computer A first image data acquisition unit that obtains first image data indicating a first image obtained by photographing a target area from a satellite, A calculation unit that calculates a vegetation index in the target area using the first image data, A determination unit that determines whether the vegetation index meets a predetermined evaluation criterion, A second image data acquisition unit that obtains second image data indicating a second image obtained by photographing the target area at a higher resolution than the first image when the vegetation index does not meet the evaluation criteria, Functioning as The first image data acquisition unit acquires a plurality of first image data indicating a plurality of first images obtained by photographing the target area at a plurality of time points. The calculation unit calculates the vegetation indices at the plurality of time points using the plurality of first image data. The determination unit determines whether a statistic of the vegetation indices at the plurality of time points satisfies the evaluation criteria, a data acquisition program.

13. Executed by a computer, the computer is caused to a first image data acquisition unit that acquires first image data indicating a first image obtained by photographing a target area from a satellite; a calculation unit that calculates a vegetation index in the target area using the first image data; a determination unit that determines whether the vegetation index satisfies a predetermined evaluation criterion; a second image data acquisition unit that, when the vegetation index does not satisfy the evaluation criterion, acquires second image data indicating a second image obtained by photographing the target area with a higher resolution than the first image; a specifying unit that specifies the resolution of the second image according to the vegetation index; function, a data acquisition program.

Images