A soil and water loss monitoring method and system based on multi-source data

Abstract

The present invention discloses a soil erosion monitoring method and system based on multi-source data, the method comprising: obtaining at least one remote sensing image of a to-be-monitored area within a preset time period, comparing the at least one remote sensing image according to a preset comparison strategy, being able to quickly determine a risk area for soil erosion, and directly obtaining real-time dimensional information of calibration points in the risk area, compared with the traditional method of monitoring the real-time dimensional information of all calibration points, being able to quickly determine the risk area while reducing the amount of data processing as much as possible, and according to the current dimensional information of each calibration point in the same sliding window, using a preset division strategy to divide the soil erosion area in the target abnormal remote sensing image. The correction of the risk area is achieved, so that a more accurate soil erosion area division result can be obtained.

Description

Technical Field

[0001] The invention belongs to the technical field of soil and water loss data processing, and in particular relates to a soil and water loss monitoring method and system based on multi-source data. Background Art

[0002] Soil erosion refers to the phenomenon that due to natural or human factors, rainwater cannot be absorbed locally, but flows downstream and washes the soil, causing the simultaneous loss of water and soil.

[0003] In the prior art, soil erosion is generally assessed by taking photos of the scene and then comparing them with photos taken a year ago (or a certain period of time ago) to determine whether there is serious soil erosion. However, it is impossible to accurately obtain the soil erosion area, which makes it inconvenient to carry out work in the soil erosion area. Summary of the invention

[0004] The present invention provides a soil erosion monitoring method and system based on multi-source data, which are used to solve the technical problem that the soil erosion area cannot be obtained more accurately.

[0005] In a first aspect, the present invention provides a soil and water loss monitoring method based on multi-source data, comprising:

[0006] Acquire at least one remote sensing image of the area to be monitored within a preset time period;

[0007] Comparing the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image includes a normal area and a risk area, and calibration points are set in both the normal area and the risk area;

[0008] Determine at least one risk area in each abnormal remote sensing image, and fuse the at least one risk area based on a preset fusion rule to obtain at least one target risk area;

[0009] Sliding on the boundary of the at least one target risk area based on a preset sliding window, and acquiring current dimension information of each calibration point in the sliding window each time the sliding window is slid;

[0010] According to the current dimension information of each calibration point in the same sliding window, a preset division strategy is adopted to divide the soil and water loss area in the target abnormal remote sensing image.

[0011] In a second aspect, the present invention provides a soil and water loss monitoring system based on multi-source data, comprising:

[0012] An acquisition module, configured to acquire at least one remote sensing image of the area to be monitored within a preset time period;

[0013] A comparison module is configured to compare the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image includes a normal area and a risk area, and calibration points are set in the normal area and the risk area;

[0014] A fusion module is configured to determine at least one risk area in each abnormal remote sensing image, and fuse the at least one risk area based on a preset fusion rule to obtain at least one target risk area;

[0015] A sliding module, configured to slide on the boundary of the at least one target risk area based on a preset sliding window, and to obtain current dimension information of each calibration point in the sliding window each time the sliding window is slid;

[0016] The division module is configured to divide the soil erosion area in the target abnormal remote sensing image according to the current dimension information of each calibration point in the same sliding window and adopt a preset division strategy.

[0017] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the soil and water loss monitoring method based on multi-source data of any embodiment of the present invention.

[0018] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the soil and water loss monitoring method based on multi-source data of any embodiment of the present invention.

[0019] The soil and water loss monitoring method and system based on multi-source data of the present application obtains at least one remote sensing image of the area to be monitored within a preset time period, and compares the at least one remote sensing image according to a preset comparison strategy, so as to quickly determine the risk area of ​​soil and water loss, and directly obtain the real-time dimensional information of the calibration points in the risk area. Compared with the traditional monitoring of the real-time dimensional information of all calibration points, it can reduce the data processing amount as much as possible while determining the risk area more quickly, and according to the current dimensional information of each calibration point in the same sliding window, a preset division strategy is adopted to divide the soil and water loss area in the target abnormal remote sensing image, so as to realize the correction of the risk area, so as to obtain a more accurate soil and water loss area division result. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a soil and water loss monitoring method based on multi-source data provided by an embodiment of the present invention;

[0022] Figure 2 A structural block diagram of a soil and water loss monitoring system based on multi-source data provided by an embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 , which shows a flow chart of a soil and water loss monitoring method based on multi-source data of the present application.

[0026] like Figure 1 As shown in the figure, the soil and water loss monitoring methods based on multi-source data include:

[0027] Step S101, acquiring at least one remote sensing image of a to-be-monitored area within a preset time period.

[0028] Step S102, comparing the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image includes a normal area and a risk area, and calibration points are set in the normal area and the risk area.

[0029] In this step, the first remote sensing image and the second remote sensing image, and the first remote sensing image and the third remote sensing image are grouped and input into a preset image recognition model, and the image recognition model outputs a first similarity corresponding to the second remote sensing image, and a second similarity corresponding to the third remote sensing image, wherein the first acquisition time of the first remote sensing image, the second acquisition time of the second remote sensing image, and the third acquisition time of the third remote sensing image are sequentially adjacent;

[0030] Determining whether the first similarity is greater than a first preset threshold, and whether a target difference between the second similarity and the first similarity is greater than a second preset threshold;

[0031] If the first similarity is greater than a first preset threshold, and the target difference is greater than a second preset threshold, defining both the second remote sensing image and the third remote sensing image as abnormal remote sensing images;

[0032] If the first similarity is greater than a first preset threshold value, and the target difference is not greater than a second preset threshold value, only the second remote sensing image is defined as an abnormal remote sensing image;

[0033] If the first similarity is not greater than a first preset threshold, and the target difference is greater than a second preset threshold, only the third remote sensing image is defined as an abnormal remote sensing image;

[0034] If the first similarity is not greater than the first preset threshold and the target difference is not greater than the second preset threshold, the second remote sensing image and the third remote sensing image are both defined as normal remote sensing images, and the second remote sensing image and the third remote sensing image, and the second remote sensing image and the fourth remote sensing image are grouped and input into a preset image recognition model, wherein the fourth acquisition time of the fourth remote sensing image is after and adjacent to the third acquisition time.

[0035] It should be noted that the image recognition model can be obtained by training a convolutional neural network.

[0036] Step S103: determining at least one risk area in each abnormal remote sensing image, and fusing the at least one risk area based on a preset fusion rule to obtain at least one target risk area.

[0037] In this step, each abnormal remote sensing image is sorted based on the acquisition time to obtain an abnormal remote sensing image sequence, and the last abnormal remote sensing image in the abnormal remote sensing image sequence is defined as the target abnormal remote sensing image; at least one risk area in the target abnormal remote sensing image is identified based on the image recognition model, and it is determined whether the distance between the first center point of the first risk area and the second center point of the second risk area is greater than a preset distance threshold; if it is not greater than the preset distance threshold, a target circle of a preset radius is constructed, and the center of the target circle is moved on the line between the first center point and the second center point, wherein the value of the preset radius is: the minimum value of the first minimum distance from the first center point to the edge of the first risk area, and the second minimum distance from the second center point to the edge of the second risk area; the area covered when the target circle moves is merged with the first risk area and the second risk area to obtain the target risk area.

[0038] In this embodiment, since soil erosion is caused by the transfer of soil and water, the transfer process is a continuous action process. Therefore, it is assumed that the two adjacent risk areas identified are not in contact, but the two adjacent risk areas are relatively close to each other. In the subsequent soil and water transfer process, there is a high probability that soil erosion will occur again between the two adjacent risk areas. Therefore, the method of this embodiment is adopted to construct a target circle with a preset radius, and the center of the target circle is moved on the line between the first center point and the second center point. The area covered by the target circle when it moves is merged with the first risk area and the second risk area to obtain the target risk area, so that the target risk area can be determined earlier, which is convenient for subsequent analysis.

[0039] In a specific embodiment, after determining whether the distance between the first center point of the first risk area and the second center point of the second risk area is greater than a preset distance threshold, if it is greater than the preset distance threshold, the first risk area and the second risk area are not fused, and at least one risk area in the target abnormal remote sensing image is directly defined as a target risk area.

[0040] Step S104: Slide on the boundary of the at least one target risk area based on a preset sliding window, and obtain current dimension information of each calibrated point in the sliding window each time the sliding window is slid.

[0041] In this step, the dimensional information includes the height information of the calibration point.

[0042] Step S105, dividing the soil and water loss area in the target abnormal remote sensing image by using a preset division strategy according to the current dimension information of each calibration point in the same sliding window.

[0043] In this step, the current dimension information of each calibration point in the same sliding window is obtained, and the first calibration point and the second calibration point with the largest dimension difference are screened out, wherein the first calibration point and the second calibration point are two adjacent calibration points; the midpoint between the first calibration point and the second calibration point is taken as the dividing point, and the dividing points in each sliding window are connected in sequence to obtain a closed dividing line, that is, the final soil erosion area is obtained.

[0044] In this embodiment, the soil erosion area can be effectively determined by combining remote sensing image information with dimensional information, and the soil erosion area can be corrected by the steps performed in step S105, thereby obtaining a more accurate soil erosion area division result.

[0045] In summary, the method of the present application obtains at least one remote sensing image of the area to be monitored within a preset time period, and compares the at least one remote sensing image according to a preset comparison strategy, so as to quickly determine the risk area of ​​soil erosion, and directly obtain the real-time dimensional information of the calibration points in the risk area. Compared with the traditional real-time dimensional information of all calibration points monitored, it can reduce the amount of data processing as much as possible while determining the risk area more quickly, and according to the current dimensional information of each calibration point in the same sliding window, a preset division strategy is adopted to divide the soil erosion area in the target abnormal remote sensing image, thereby realizing the correction of the risk area, so as to obtain a more accurate soil erosion area division result.

[0046] See also Figure 2 , which shows a structural block diagram of a soil and water loss monitoring system based on multi-source data of the present application.

[0047] like Figure 2 As shown, the soil and water loss monitoring system 200 includes an acquisition module 210 , a comparison module 220 , a fusion module 230 , a sliding module 240 and a division module 250 .

[0048] Among them, the acquisition module 210 is configured to acquire at least one remote sensing image of the area to be monitored within a preset time period; the comparison module 220 is configured to compare the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image contains normal areas and risk areas, and calibration points are set in the normal areas and risk areas; the fusion module 230 is configured to determine at least one risk area in each abnormal remote sensing image, and fuse the at least one risk area based on a preset fusion rule to obtain at least one target risk area; the sliding module 240 is configured to slide on the boundary of the at least one target risk area based on a preset sliding window, and obtain the current dimension information of each calibration point in the sliding window each time it slides; the division module 250 is configured to divide the soil and water loss area in the target abnormal remote sensing image according to the current dimension information of each calibration point in the same sliding window using a preset division strategy.

[0049] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0050] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the soil and water loss monitoring method based on multi-source data in any of the above method embodiments;

[0051] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0052] Acquire at least one remote sensing image of the area to be monitored within a preset time period;

[0053] Comparing the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image includes a normal area and a risk area, and calibration points are set in both the normal area and the risk area;

[0054] Determine at least one risk area in each abnormal remote sensing image, and fuse the at least one risk area based on a preset fusion rule to obtain at least one target risk area;

[0055] Sliding a preset sliding window on the boundary of the at least one target risk area, and acquiring current dimension information of each calibrated point in the sliding window each time the sliding window is slid;

[0056] According to the current dimension information of each calibration point in the same sliding window, a preset division strategy is adopted to divide the soil and water loss area in the target abnormal remote sensing image.

[0057] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the soil and water loss monitoring system based on multi-source data, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the soil and water loss monitoring system based on multi-source data via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0058] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the bus connection is used. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the soil and water loss monitoring method based on multi-source data of the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the soil and water loss monitoring system based on multi-source data. The output device 340 may include display devices such as display screens.

[0059] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0060] As an implementation mode, the electronic device is applied to a soil and water loss monitoring system based on multi-source data, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0061] Acquire at least one remote sensing image of the area to be monitored within a preset time period;

[0062] Comparing the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image includes a normal area and a risk area, and calibration points are set in both the normal area and the risk area;

[0063] Determine at least one risk area in each abnormal remote sensing image, and fuse the at least one risk area based on a preset fusion rule to obtain at least one target risk area;

[0064] Sliding a preset sliding window on the boundary of the at least one target risk area, and acquiring current dimension information of each calibrated point in the sliding window each time the sliding window is slid;

[0065] According to the current dimension information of each calibration point in the same sliding window, a preset division strategy is adopted to divide the soil and water loss area in the target abnormal remote sensing image.

[0066] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A soil and water loss monitoring method based on multi-source data, characterized in that: include: Acquire at least one remote sensing image of the area to be monitored within a preset time period; Comparing the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image includes a normal area and a risk area, and calibration points are set in both the normal area and the risk area; Determine at least one risk area in each abnormal remote sensing image, and fuse the at least one risk area based on a preset fusion rule to obtain at least one target risk area, wherein determining at least one risk area in each abnormal remote sensing image, and fusing the at least one risk area based on a preset fusion rule to obtain at least one target risk area includes: Sorting each abnormal remote sensing image based on the acquisition time to obtain an abnormal remote sensing image sequence, and defining the last abnormal remote sensing image in the abnormal remote sensing image sequence as a target abnormal remote sensing image; Identify at least one risk area in the target abnormal remote sensing image based on the image recognition model, and determine whether a distance between a first center point of the first risk area and a second center point of the second risk area is greater than a preset distance threshold; If it is not greater than a preset distance threshold, construct a target circle of a preset radius, and move the center of the target circle on the line between the first center point and the second center point, wherein the value of the preset radius is: the minimum value of a first minimum distance from the first center point to the edge of the first risk area and a second minimum distance from the second center point to the edge of the second risk area; Merging the area covered by the target circle when it moves with the first risk area and the second risk area to obtain a target risk area; Sliding a preset sliding window on the boundary of the at least one target risk area, and acquiring current dimension information of each calibrated point in the sliding window each time the sliding window is slid; According to the current dimension information of each calibration point in the same sliding window, a preset division strategy is adopted to divide the soil and water loss area in the target abnormal remote sensing image, wherein according to the current dimension information of each calibration point in the same sliding window, a preset division strategy is adopted to divide the soil and water loss area in the target abnormal remote sensing image, including: Acquire current dimension information of each calibration point in the same sliding window, and filter out a first calibration point and a second calibration point with the largest dimension difference, wherein the first calibration point and the second calibration point are two adjacent calibration points; The midpoint between the first calibration point and the second calibration point is taken as the dividing point, and the dividing points in each sliding window are connected in sequence to obtain a closed dividing line, that is, the final soil and water loss area.

2. The method for monitoring soil and water loss based on multi-source data according to claim 1, characterized in that: Comparing the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image includes: The first remote sensing image and the second remote sensing image, and the first remote sensing image and the third remote sensing image are grouped and input into a preset image recognition model, wherein the image recognition model outputs a first similarity corresponding to the second remote sensing image and a second similarity corresponding to the third remote sensing image, wherein a first acquisition time of the first remote sensing image, a second acquisition time of the second remote sensing image, and a third acquisition time of the third remote sensing image are sequentially adjacent; Determining whether the first similarity is greater than a first preset threshold, and whether a target difference between the second similarity and the first similarity is greater than a second preset threshold; If the first similarity is greater than a first preset threshold, and the target difference is greater than a second preset threshold, defining both the second remote sensing image and the third remote sensing image as abnormal remote sensing images; If the first similarity is greater than a first preset threshold and the target difference is not greater than a second preset threshold, only the second remote sensing image is defined as an abnormal remote sensing image.

3. The method for monitoring soil and water loss based on multi-source data according to claim 2, characterized in that: After determining whether the first similarity is greater than a first preset threshold and whether a target difference between the second similarity and the first similarity is greater than a second preset threshold, the method further includes: If the first similarity is not greater than a first preset threshold, and the target difference is greater than a second preset threshold, only the third remote sensing image is defined as an abnormal remote sensing image; If the first similarity is not greater than the first preset threshold and the target difference is not greater than the second preset threshold, the second remote sensing image and the third remote sensing image are both defined as normal remote sensing images, and the second remote sensing image and the third remote sensing image, and the second remote sensing image and the fourth remote sensing image are grouped and input into a preset image recognition model, wherein the fourth acquisition time of the fourth remote sensing image is after and adjacent to the third acquisition time.

4. The method for monitoring soil and water loss based on multi-source data according to claim 1, characterized in that: After determining whether the distance between the first center point of the first risk area and the second center point of the second risk area is greater than a preset distance threshold, the method further includes: If it is greater than a preset distance threshold, the first risk area and the second risk area are not merged, and at least one risk area in the target abnormal remote sensing image is directly defined as a target risk area.

5. A soil and water loss monitoring system based on multi-source data, characterized in that: include: An acquisition module, configured to acquire at least one remote sensing image of the area to be monitored within a preset time period; A comparison module is configured to compare the at least one remote sensing image according to a preset comparison strategy to obtain at least one abnormal remote sensing image, wherein the abnormal remote sensing image includes a normal area and a risk area, and calibration points are set in the normal area and the risk area; A fusion module is configured to determine at least one risk area in each abnormal remote sensing image, and fuse the at least one risk area based on a preset fusion rule to obtain at least one target risk area, wherein the determining at least one risk area in each abnormal remote sensing image, and fusing the at least one risk area based on a preset fusion rule to obtain at least one target risk area includes: Sorting each abnormal remote sensing image based on the acquisition time to obtain an abnormal remote sensing image sequence, and defining the last abnormal remote sensing image in the abnormal remote sensing image sequence as a target abnormal remote sensing image; Identify at least one risk area in the target abnormal remote sensing image based on the image recognition model, and determine whether a distance between a first center point of the first risk area and a second center point of the second risk area is greater than a preset distance threshold; If it is not greater than a preset distance threshold, construct a target circle of a preset radius, and move the center of the target circle on the line between the first center point and the second center point, wherein the value of the preset radius is: the minimum value of a first minimum distance from the first center point to the edge of the first risk area and a second minimum distance from the second center point to the edge of the second risk area; Merging the area covered by the target circle when it moves with the first risk area and the second risk area to obtain a target risk area; A sliding module, configured to slide on the boundary of the at least one target risk area based on a preset sliding window, and to obtain current dimension information of each calibration point in the sliding window each time the sliding window is slid; The division module is configured to divide the soil and water loss area in the target abnormal remote sensing image according to the current dimension information of each calibration point in the same sliding window by using a preset division strategy, wherein the division of the soil and water loss area in the target abnormal remote sensing image according to the current dimension information of each calibration point in the same sliding window by using a preset division strategy includes: Acquire current dimension information of each calibration point in the same sliding window, and filter out a first calibration point and a second calibration point with the largest dimension difference, wherein the first calibration point and the second calibration point are two adjacent calibration points; The midpoint between the first calibration point and the second calibration point is taken as the dividing point, and the dividing points in each sliding window are connected in sequence to obtain a closed dividing line, that is, the final soil and water loss area.

6. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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