Wetland restoration state identification method and system based on remote sensing technology

Abstract

The invention discloses a wetland restoration state identification method and system based on a remote sensing technology, and relates to the field of image data processing. The method comprises the following steps: acquiring a remote sensing image corresponding to a target area; preprocessing the remote sensing image to obtain a first to-be-processed image; edge detection is carried out on the first to-be-processed image, a closed area formed by each edge is recorded as an area to be detected, and a second to-be-processed image is obtained; performing feature detection on the second to-be-processed image, identifying a field area in the remote sensing image, and excluding the field area from the remote sensing image to obtain a third to-be-processed image; performing classification based on the third to-be-processed image to obtain a wetland area; and determining the wetland restoration state of the target area according to the range change condition of the wetland area. According to the method and the system provided by the invention, the interference of the field in the remote sensing image on wetland classification can be reduced, so that the accuracy of wetland restoration state recognition is improved.

Description

Technical Field

[0001] The present application relates to the field of image data processing, and more specifically, to a method and system for identifying wetland restoration status based on remote sensing technology. Background Art

[0002] Wetlands are important natural resources and land types, and are also the concrete manifestation of the natural ecological environment. In the 20 years from the end of the 20th century to the beginning of the 21st century, my country's wetland ecological environment has suffered a lot of damage. Therefore, it is extremely necessary to strengthen the protection of wetlands.

[0003] When protecting the wetland ecological environment, remote sensing technology can be used to conduct real-time and dynamic monitoring of wetlands and their surroundings to identify the wetland restoration status. At present, the iterative self-organizing clustering method is generally used to divide the wetland range in remote sensing images for relevant monitoring. However, when there are fields near the wetland, because the color of the field is close to the color of the wetland, the two have similar terrain, band reflection and other characteristics in the remote sensing image. It is difficult to use the iterative self-organizing clustering method to distinguish between fields and wetlands that are very close and have similar colors, and it is easy to mistakenly classify the fields into the wetland range.

[0004] Based on this, it is necessary to study a more accurate and reliable wetland restoration status identification method and system based on remote sensing technology to reduce the interference of fields on wetland classification and improve the accuracy of wetland restoration status identification results. Summary of the invention

[0005] To solve the above problems, one aspect of an embodiment of this specification provides a method for identifying wetland restoration status based on remote sensing technology, the method comprising:

[0006] Acquire a remote sensing image corresponding to the target area, wherein the remote sensing image is acquired within a specified time period;

[0007] Preprocessing the remote sensing image to obtain a first image to be processed;

[0008] Performing edge detection on the first image to be processed, and recording each closed area formed by an edge as a detected area, to obtain a second image to be processed;

[0009] Performing feature detection on the second image to be processed, identifying a field area in the remote sensing image, and excluding the field area from the remote sensing image to obtain a third image to be processed;

[0010] Performing classification based on the third image to be processed to obtain a wetland area;

[0011] The wetland restoration status of the target area is determined based on changes in the scope of the wetland area.

[0012] In some embodiments, the performing feature detection on the second image to be processed to identify the field area in the remote sensing image includes:

[0013] Detecting the color characteristics of the area to be tested to obtain a suspected field area;

[0014] According to the edge shape characteristics of the suspected field area, the suspected field area is divided into a field area, a questionable area and a non-field area;

[0015] The surrounding area of ​​the question area is identified, and it is determined whether the question area belongs to a field area or a non-field area based on the situation of the adjacent areas around the question area.

[0016] In some embodiments, detecting the color characteristics of the area to be detected to obtain a suspected field area includes:

[0017] For each area to be tested, the total number of pixels in the area to be tested and the grayscale value of each pixel are obtained;

[0018] Calculating the similarity of each pixel in the area to be tested to the overall color of the area to be tested;

[0019] Based on the similarity, calculating the color consistency within the tested area;

[0020] The area to be tested whose color consistency is greater than a first preset threshold is marked as a suspected field area.

[0021] In some embodiments, the similarity of each pixel in the region to be tested to the overall color of the region to be tested and the color consistency in the region to be tested are obtained based on the following method:

[0022] Calculate the average gray value in the area to be tested;

[0023] Obtaining a first parameter based on a ratio of a grayscale value of each pixel in the area to be measured to the average grayscale value;

[0024] Normalizing the first parameter to obtain the similarity of each pixel point to the overall color of the area to be measured;

[0025] The average value of the similarity of all pixels in the area to be tested relative to the overall color of the area to be tested is calculated to obtain the color consistency in the area to be tested.

[0026] In some embodiments, dividing the suspected field area into a field area, a questionable area, and a non-field area according to the edge shape characteristics of the suspected field area includes:

[0027] Obtaining the major diameter, minor diameter, number of pixels and pixel length of the edge of the suspected field area;

[0028] Based on the major diameter, minor diameter and number of pixels corresponding to the suspected field area, calculating the area similarity between the suspected field area and the rectangle;

[0029] Calculating the pixel length difference of the relative edge based on the pixel length of the edge of the area corresponding to the suspected field area;

[0030] Determining the shape representation of the suspected field area according to the area similarity and the pixel length difference of the relative edges;

[0031] The suspected field area whose shape performance is less than the second preset threshold is classified as a non-field area; the suspected field area whose shape performance is greater than the third preset threshold is classified as a field area; the suspected field area whose shape performance is greater than or equal to the second preset threshold and less than or equal to the third preset threshold is classified as a questionable area.

[0032] In some embodiments, determining the shape of the suspected field area according to the area similarity and the pixel length difference of the relative edges includes:

[0033] The pixel length differences of the relative edges are normalized, and the product of the normalization result and the area similarity is used as the shape representation of the suspected field area.

[0034] In some embodiments, the identifying of the surrounding area of ​​the question area and determining whether the question area is a field area or a non-field area based on the situation of the adjacent area around the question area includes:

[0035] Based on the distance from the center of each adjacent region to the center of the question region and the average of the distances from the center of each adjacent region to the center of the question region, the distance performance of each adjacent region relative to other adjacent regions is obtained;

[0036] Based on the shape performance of each adjacent region and the maximum and minimum values ​​of the shape performances corresponding to all adjacent regions adjacent to the question region, the shape performance difference of each adjacent region relative to the surrounding adjacent regions of the question region is obtained;

[0037] Based on the distance performance of each adjacent region relative to other adjacent regions and the difference in shape performance of each adjacent region relative to the surrounding adjacent regions of the question region, a normalized feature value corresponding to each adjacent region is obtained;

[0038] Calculate the average of normalized feature values ​​corresponding to all adjacent regions adjacent to the question region to obtain feature representation of the question region;

[0039] The question area whose characteristic performance is greater than or equal to a fourth preset threshold is marked as a field area, and the question area whose characteristic performance is less than the fourth preset threshold is marked as a non-field area.

[0040] In some embodiments, the distance representation of the adjacent region relative to other adjacent regions is obtained based on the following method:

[0041] For the adjacent region m adjacent to the question region O, calculate the distance l from the center of the adjacent region m to the center of the question region O m The average value of the distance from the center of each adjacent area to the center of the question area O Ratio

[0042] The ratio It is expressed as the distance between the adjacent area m and other adjacent areas.

[0043] In some embodiments, the shape difference between the adjacent region and the surrounding adjacent region of the question region is obtained based on the following method:

[0044] For the adjacent region m adjacent to the question region O, calculate the shape representation X of the adjacent region m m The minimum value min(X) of the shape expression corresponding to all adjacent regions adjacent to the query region O O,t ), and the shape representation maximum value max(X O,t ) and the minimum value min(X O,t )’s second difference;

[0045] The ratio of the first difference to the second difference is used as the shape representation difference of the adjacent region m.

[0046] Another aspect of the embodiments of this specification also provides a wetland restoration status identification system based on remote sensing technology, the system comprising:

[0047] An acquisition module is used to acquire a remote sensing image corresponding to a target area, wherein the remote sensing image is acquired within a specified time period;

[0048] A preprocessing module, used for preprocessing the remote sensing image to obtain a first image to be processed;

[0049] An edge detection module is used to perform edge detection on the first image to be processed, and record each closed area formed by an edge as a detected area, so as to obtain a second image to be processed;

[0050] a feature detection module, configured to perform feature detection on the second image to be processed, identify a field area in the remote sensing image, and exclude the field area from the remote sensing image to obtain a third image to be processed;

[0051] A classification module, used for performing classification based on the third image to be processed to obtain a wetland area;

[0052] The wetland restoration status determination module is used to determine the wetland restoration status of the target area according to the range change of the wetland area.

[0053] The beneficial effects of the wetland restoration status identification method and system based on remote sensing technology provided in the embodiments of this specification may include at least:

[0054] (1) By excluding the field area in the remote sensing image and then performing wetland identification on the remaining image, the interference of the field in the remote sensing image on the wetland classification can be effectively reduced, thereby improving the accuracy of wetland restoration status identification;

[0055] (2) By identifying the field areas in the remote sensing image based on color features, shape features and the conditions of the surrounding adjacent areas, the field areas in the remote sensing image can be accurately and effectively identified.

[0056] Additional features will be described in part in the following description. For those skilled in the art, the invention of the present application will become apparent by reviewing the following and accompanying drawings, or can be understood by the production or operation of the examples. The features of this specification can be implemented and obtained by practicing or using various aspects of the methods, tools and combinations described in the following detailed examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0058] Figure 1 is a schematic diagram of an exemplary application scenario of a wetland restoration status identification system based on remote sensing technology according to some embodiments of this specification;

[0059] Figure 2 is an exemplary module diagram of a wetland restoration status identification system based on remote sensing technology according to some embodiments of this specification;

[0060] Figure 3 is an exemplary flow chart of a wetland restoration status identification method based on remote sensing technology according to some embodiments of this specification;

[0061] Figure 4 is a schematic diagram of the preprocessing image effect shown in some embodiments of this specification;

[0062] Figure 5 It is a schematic diagram of edge detection image effect according to some embodiments of this specification. DETAILED DESCRIPTION

[0063] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, without paying creative work, this specification can also be applied to other similar scenarios based on these drawings. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0064] It should be understood that the "system", "device", "unit" and / or "module" used in this specification is a method for distinguishing different components, elements, parts, parts or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0065] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0066] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0067] The wetland restoration status identification method and system based on remote sensing technology provided in the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0068] Figure 1 It is a schematic diagram of an exemplary application scenario of a wetland restoration status identification system based on remote sensing technology according to some embodiments of this specification.

[0069] Reference Figure 1 In some embodiments, the application scenario 100 of the wetland restoration status identification system based on remote sensing technology may include a remote sensing image acquisition device 110, a storage device 120, a processing device 130, a terminal device 140, and a network 150. The various components in the application scenario 100 can be connected in a variety of ways. For example, the remote sensing image acquisition device 110 can be connected to the storage device 120 and / or the processing device 130 through the network 150, or directly connected to the storage device 120 and / or the processing device 130. For another example, the storage device 120 can be directly connected to the processing device 130 or connected through the network 150. For another example, the terminal device 140 can be connected to the storage device 120 and / or the processing device 130 through the network 150, or directly connected to the storage device 120 and / or the processing device 130.

[0070] The remote sensing image acquisition device 110 can be used to acquire a remote sensing image corresponding to the target area, wherein the remote sensing image is acquired within a specified time period, and the remote sensing image can reflect the wetland conditions of the target area. In some embodiments, the remote sensing image acquisition device 110 can acquire a remote sensing image of the target area through GF-2 (GF-2). In some embodiments, the remote sensing image can be acquired through GF-2 within a specified time period (e.g., a time period when field vegetation grows well), thereby ensuring that the wetland area and field area in the remote sensing image have more obvious features to facilitate classification research. In some embodiments, the remote sensing image acquisition device 110 can have an independent power supply, which can send the acquired remote sensing image to other components (e.g., storage device 120, processing device 130, terminal device 140) in the application scenario 100 by wired or wireless (e.g., Bluetooth, WiFi, etc.).

[0071] In some embodiments, the remote sensing image acquisition device 110 can send the remote sensing image it acquires to the storage device 120, the processing device 130, the terminal device 140, etc. through the network 150. In some embodiments, the remote sensing image acquired by the remote sensing image acquisition device 110 can be processed by the processing device 130. For example, the processing device 130 can determine the field area in the target area based on the remote sensing image, exclude the field area from the remote sensing image, and then classify the image after excluding the field area to obtain the wetland area. In some embodiments, the data processed by the processing device 130 can be sent to the storage device 120 for recording, or sent to the terminal device 140 for feedback to the user.

[0072] The network 150 may facilitate the exchange of information and / or data. The network 150 may include any suitable network capable of facilitating the exchange of information and / or data of the application scenario 100. In some embodiments, at least one component of the application scenario 100 (e.g., the remote sensing image acquisition device 110, the storage device 120, the processing device 130, the terminal device 140) may exchange information and / or data with at least one other component in the application scenario 100 via the network 150. For example, the processing device 130 may obtain a remote sensing image acquired for a target area from the remote sensing image acquisition device 110 and / or the storage device 120 via the network 150. For another example, the processing device 130 may obtain a user operation instruction from the terminal device 140 via the network 150, and exemplary operation instructions may include but are not limited to calling up a remote sensing image, reading a field area and / or a wetland area determined based on the remote sensing image, and the like.

[0073] In some embodiments, the network 150 may be any form of wired or wireless network, or any combination thereof. By way of example only, the network 150 may include a cable network, a wired network, an optical fiber network, a telecommunications network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, etc., or any combination thereof. In some embodiments, the network 150 may include at least one network access point, and at least one component of the application scenario 100 may be connected to the network 150 through the access point to exchange data and / or information.

[0074] The storage device 120 may store data, instructions and / or any other information. In some embodiments, the storage device 120 may store data obtained from the remote sensing image acquisition device 110, the processing device 130 and / or the terminal device 140. For example, the storage device 120 may store the remote sensing image acquired by the remote sensing image acquisition device 110; for another example, the storage device 120 may store the data processed by the processing device 130 (such as the first image to be processed, the second image to be processed, the third image to be processed, etc. mentioned later). In some embodiments, the storage device 120 may store data and / or instructions used by the processing device 130 to execute or use to complete the exemplary method described in this specification. In some embodiments, the storage device 120 may include a large capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. Exemplary large capacity memory may include a magnetic disk, an optical disk, a solid-state disk, etc. In some embodiments, the storage device 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof.

[0075] In some embodiments, the storage device 120 may be connected to the network 150 to communicate with at least one other component in the application scenario 100 (e.g., the remote sensing image acquisition device 110, the processing device 130, the terminal device 140). At least one component in the application scenario 100 may access data, instructions, or other information stored in the storage device 120 through the network 150. In some embodiments, the storage device 120 may be directly connected or communicated with one or more components in the application scenario 100 (e.g., the remote sensing image acquisition device 110, the terminal device 140). In some embodiments, the storage device 120 may be a part of the remote sensing image acquisition device 110 and / or the processing device 130.

[0076] The processing device 130 can process data and / or information obtained from the remote sensing image acquisition device 110, the storage device 120, the terminal device 140 and / or other components of the application scenario 100. In some embodiments, the processing device 130 can obtain a remote sensing image from any one or more of the remote sensing image acquisition device 110, the storage device 120 or the terminal device 140, process the remote sensing image to determine the field area in the target area, exclude the field area from the remote sensing image, and then classify the image after excluding the field area to obtain the wetland area. In some embodiments, the processing device 130 can obtain a pre-stored computer instruction from the storage device 120, and execute the computer instruction to implement the wetland restoration status identification method based on remote sensing technology described in this specification.

[0077] In some embodiments, the processing device 130 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 130 may be local or remote. For example, the processing device 130 may access information and / or data from the remote sensing image acquisition device 110, the storage device 120, and / or the terminal device 140 via the network 150. For another example, the processing device 130 may be directly connected to the remote sensing image acquisition device 110, the storage device 120, and / or the terminal device 140 to access information and / or data. In some embodiments, the processing device 130 may be implemented on a cloud platform. For example, a cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud cloud, a multi-cloud, etc., or any combination thereof.

[0078] The terminal device 140 can receive, send and / or display data. The received data may include data collected by the remote sensing image acquisition device 110, data stored in the storage device 120, data processed by the processing device 130, etc. For example, the data received and / or displayed by the terminal device 140 may include remote sensing images acquired by the remote sensing image acquisition device 110, field areas, wetland areas, etc. determined by the processing device 130 based on the remote sensing images. The sent data may include user input data and instructions, etc. For example, the terminal device 140 may send the operation instructions input by the user to the remote sensing image acquisition device 110 through the network 150 to control the remote sensing image acquisition device 110 to perform corresponding remote sensing image acquisition. For another example, the terminal device 140 may send the processing instructions input by the user to the processing device 130 through the network 150 to control the processing device 130 to perform corresponding data processing.

[0079] In some embodiments, the terminal device 140 may include a mobile device 141, a tablet computer 142, a laptop computer 143, etc., or any combination thereof. For example, the mobile device 141 may include a mobile phone, a personal digital assistant (PDA), a dedicated mobile terminal, etc., or any combination thereof. In some embodiments, the terminal device 140 may include an input device (such as a keyboard, a touch screen), an output device (such as a display, a speaker), etc. In some embodiments, the processing device 130 may be a part of the terminal device 140.

[0080] It should be noted that the above description of the application scenario 100 is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the application scenario 100 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, the remote sensing image acquisition device 110 may include more or fewer functional components.

[0081] Figure 2 is a schematic diagram of a module of a wetland restoration status identification system based on remote sensing technology according to some embodiments of this specification. In some embodiments, Figure 2 The wetland restoration status identification system 200 based on remote sensing technology can be applied to Figure 1 The application scenario 100 shown, for example, can be configured in the form of software and / or hardware to the processing device 130 and / or the terminal device 140 to process the remote sensing image acquired by the remote sensing image acquisition device 110, and determine the field area in the target area based on the remote sensing image, and then exclude the field area from the remote sensing image, and finally classify the image after excluding the field area to obtain the wetland area.

[0082] Reference Figure 2In some embodiments, the wetland restoration status identification system 200 based on remote sensing technology may include an acquisition module 210, a preprocessing module 220, an edge detection module 230, a feature detection module 240, a classification module 250 and a wetland restoration status determination module 260.

[0083] The acquisition module 210 may be used to acquire a remote sensing image corresponding to a target area, wherein the remote sensing image is acquired within a specified time period.

[0084] The preprocessing module 220 can be used to preprocess the remote sensing image to obtain a first image to be processed.

[0085] The edge detection module 230 may be used to perform edge detection on the first image to be processed, and record each closed area formed by an edge as a detected area to obtain a second image to be processed.

[0086] The feature detection module 240 can be used to perform feature detection on the second image to be processed, identify the field area in the remote sensing image, and exclude the field area from the remote sensing image to obtain a third image to be processed.

[0087] The classification module 250 may be used to perform classification based on the third image to be processed to obtain a wetland area.

[0088] The wetland restoration status determination module 260 may be used to determine the wetland restoration status of the target area according to the range change of the wetland area.

[0089] For more details about the above modules, please refer to other places in this manual (for example Figure 3 to Figure 5 part and its related description), which will not be repeated here.

[0090] It should be understood that Figure 2The wetland restoration status identification system 200 based on remote sensing technology and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer executable instructions and / or included in processor control codes, such as such codes provided on carrier media such as disks, CDs or DVD-ROMs, programmable memories such as read-only memories (firmware), or data carriers such as optical or electronic signal carriers. The system and its modules of this specification can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0091] It should be noted that the above description of the wetland restoration status identification system 200 based on remote sensing technology is provided for illustrative purposes only and is not intended to limit the scope of this specification. It is understandable that for those skilled in the art, according to the description of this specification, without departing from this principle, the various modules can be arbitrarily combined, or a subsystem can be formed to connect with other modules. For example, Figure 2 The acquisition module 210, preprocessing module 220, edge detection module 230, feature detection module 240, classification module 250 and wetland restoration status determination module 260 described in the system may be different modules in one system, or one module may realize the functions of two or more of the above modules. For another example, the wetland restoration status identification system 200 based on remote sensing technology may also include other modules. Such variations are within the scope of protection of this specification. In some embodiments, the aforementioned modules may be part of the processing device 130 and / or the terminal device 140.

[0092] Figure 3 is an exemplary flow chart of a wetland restoration status identification method based on remote sensing technology according to some embodiments of this specification. In some embodiments, the wetland restoration status identification method 300 based on remote sensing technology can be executed by processing logic, which can include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc. or any combination thereof. In some embodiments, Figure 3One or more operations in the flowchart of the remote sensing technology-based wetland restoration status identification method 300 can be performed by Figure 1 The processing device 130 and / or the terminal device 140 shown are implemented. For example, the wetland restoration state identification method 300 based on remote sensing technology can be stored in the storage device 120 in the form of instructions, and called and / or executed by the processing device 130 and / or the terminal device 140. The execution process of the wetland restoration state identification method 300 based on remote sensing technology is described below by taking the processing device 130 as an example.

[0093] Reference Figure 3 In some embodiments, the wetland restoration status identification method 300 based on remote sensing technology may include:

[0094] Step 310 , obtaining a remote sensing image corresponding to the target area, wherein the remote sensing image is acquired within a specified time period. In some embodiments, step 310 may be performed by the acquisition module 210 .

[0095] In some embodiments, the remote sensing image acquired by the remote sensing image acquisition device 110 for the target area can be stored in the storage device 120, and the acquisition module 210 can acquire the remote sensing image corresponding to the target area from the storage device 120, wherein the remote sensing image is acquired within a specified time period. In some embodiments, the acquisition module 210 can be connected to the remote sensing image acquisition device 110 for communication, and the acquisition module 210 can directly acquire the remote sensing image corresponding to the target area from the remote sensing image acquisition device 110.

[0096] In this specification, the target area refers to an area for which the wetland restoration status needs to be identified. In some embodiments, the aforementioned specified time period may refer to a time period when field vegetation grows well, such as May to July. It can be understood that within the specified time period, both field vegetation and wetland vegetation have relatively obvious characteristics, and accordingly, the remote sensing images collected within the specified time period also have relatively obvious image features. Based on the remote sensing images collected within the specified time period, more accurate image analysis and classification can be performed.

[0097] Step 320 , preprocessing the remote sensing image to obtain a first image to be processed. In some embodiments, step 320 may be performed by the preprocessing module 220 .

[0098] After the remote sensing image corresponding to the aforementioned target area is obtained by the acquisition module 210, it can be preprocessed by grayscale conversion, filtering, etc. by the preprocessing module 220. Specifically, in some embodiments, the pixel values ​​of each channel (red, green, and blue) of the remote sensing image can be weighted averaged to obtain the grayscale value corresponding to each pixel point, thereby retaining the details and texture information of the remote sensing image as much as possible.

[0099] In some embodiments, the preprocessing module 220 may perform low-pass filtering on the image obtained after the grayscale conversion to obtain a first image to be processed. It should be noted that in the embodiments of this specification, by performing grayscale conversion and low-pass filtering on the remote sensing image, the amount of calculation in the subsequent process can be reduced to a certain extent, and the image can be made smoother and the image quality can be improved while protecting the edge information in the image.

[0100] Step 330 , edge detection is performed on the first image to be processed, and each closed area formed by an edge is recorded as a detected area, to obtain a second image to be processed. In some embodiments, step 330 may be performed by the edge detection module 230 .

[0101] Reference Figure 4 , Figure 4 is a schematic diagram of the preprocessed image effect shown in some embodiments of this specification, that is, a schematic diagram of the first image to be processed. After obtaining the first image to be processed, the edge detection module 230 can use the Canny algorithm to perform edge detection on it, and obtain the following Figure 5 Schematic diagram of edge detection image effect shown.

[0102] Furthermore, the edge detection module 230 may record each closed area formed by an edge as a region to be detected, to obtain a second image to be processed.

[0103] Step 340 , perform feature detection on the second image to be processed, identify the field area in the remote sensing image, and exclude the field area from the remote sensing image to obtain a third image to be processed. In some embodiments, step 340 may be performed by the feature detection module 240 .

[0104] After obtaining the second image to be processed through the aforementioned processing, the feature detection module 240 may perform feature detection on the second image to be processed, thereby identifying the field area in the remote sensing image.

[0105] Since in remote sensing images, crops in fields are usually dominated by humans, the crops in the fields are basically the same, and their colors in the images are single; while there are many kinds of plants in wetlands, and their colors are also different. In addition, fields are basically planned by humans and their shapes are close to rectangles, while wetlands are mostly formed naturally or have irregular shapes due to the influence of the natural environment. Based on this, in some embodiments, when fields and wetlands are mixed together, the field area in the remote sensing image can be excluded based on the above characteristics.

[0106] Based on the above rules, in some embodiments, the feature detection module 240 can first detect the color features of the aforementioned area to be tested to obtain a suspected field area; then divide the suspected field area into a field area, a question area and a non-field area according to the edge shape features of the suspected field area; finally, identify the surrounding areas of the question area, and determine whether each question area belongs to a field area or a non-field area through the situation of the adjacent areas around the question area.

[0107] Specifically, in some embodiments, for each area to be tested, the feature detection module 240 can obtain the total number of pixels in the area to be tested and the grayscale value of each pixel; then calculate the similarity of each pixel in the area to be tested relative to the overall color of the area to be tested; and based on the similarity, calculate the color consistency in the area to be tested; finally, mark the area to be tested whose color consistency is greater than a first preset threshold as a suspected field area.

[0108] It is understandable that fields are usually planted with the same crop due to artificial planning; while wetlands need a variety of plants due to the need for natural environmental diversity. Therefore, the color of fields in remote sensing images is usually relatively simple, while the color of wetlands in remote sensing images is diverse and changes frequently. Therefore, field areas usually have the same grayscale on grayscale images (that is, the grayscale of each pixel in the field area is slightly different from the average grayscale in the area); wetland areas have multiple grayscales on grayscale images and are randomly distributed (that is, the grayscale of many pixels in the wetland area is significantly different from the average grayscale in the area).

[0109] Based on the above characteristics, the following formula can be constructed to preliminarily screen out the field areas in the remote sensing image:

[0110]

[0111] Among them, S w represents the color consistency of the area w to be tested (also called color performance, which represents the relative relationship between the grayscale of the pixels in the area to be tested and the average grayscale in the area); n is the total number of pixels in the area to be tested w; h i represents the gray value of pixel i in the measured area w; exp represents an exponential function with the natural constant e as the base, which can be obtained by exp(-f(x)) to convert the f(x) part of the formula (i.e. ) to perform negative correlation normalization.

[0112] In the above formula, Represents the average gray value in the area w to be tested; Indicates the similarity between the color (grayscale) of pixel i and the overall color (average grayscale) of the measured area w, ranging from 0 to 1. The closer the value is to 1, the closer the overall color of pixel i is to the measured area w, and the closer the value is to 0, the greater the difference between the overall color of pixel i and the measured area w. That is S w It indicates the color consistency within the tested area w, and its value range is from 0 to 1. The smaller the value is, the greater the color difference within the tested area w is, the worse the color consistency is, and the smaller the possibility of belonging to the field area is; the closer the value is to 1, the smaller the color difference within the tested area w is, the higher the color consistency is, and the greater the possibility of belonging to the field area is.

[0113] Specifically, in the embodiment of the present specification, the feature detection module 240 can determine the similarity of each pixel point in the test area to the overall color of the test area, and the color consistency in the test area in the test area by:

[0114] First, the feature detection module 240 can calculate the average gray value in the area to be detected by dividing the sum of the gray values ​​of all pixels in the area to be detected by the number of pixels in the area to be detected. Then, the feature detection module 240 can obtain the first parameter based on the ratio of the gray value of each pixel point in the detected area to the average gray value. Furthermore, the feature detection module 240 can perform normalization processing on the first parameter to obtain the similarity of each pixel point to the overall color of the area to be detected. Finally, the feature detection module 240 can calculate the average value of the similarity of all pixels in the region to be detected relative to the overall color of the region to be detected. The color consistency in the area to be tested is obtained.

[0115] In some embodiments of the present specification, the area to be tested with a color consistency greater than a first preset threshold value may be marked as a suspected field area, and the area may be analyzed in subsequent steps to determine the field area in the remote sensing image corresponding to the target area. As an example only, in some embodiments, the first preset threshold value may be set to 0.7. In other words, the color consistency S w The tested area with a value greater than 0.7 may belong to the field area and needs to be further screened and checked. Therefore, in some embodiments, the color consistency S of each tested area is calculated through the above steps. w After that, all color consistency S w The areas to be tested with a value greater than 0.7 are marked as suspected field areas.

[0116] Furthermore, after obtaining the aforementioned suspected field regions, the feature detection module 240 may further divide each suspected field region into a field region, a questionable region, and a non-field region according to edge shape features of the suspected field region.

[0117] Since the source of farmland is basically artificially planned and cultivated, and it is often maintained during labor, while wetlands are mostly formed naturally, although there are artificially planned wetlands, they have deviated from the original plan due to the evolution of the natural environment. Therefore, the farmland area appears in a rectangular shape in the remote sensing image, and its area can be replaced by the rectangular area calculation formula, that is, the pixel area in the field area is roughly equal to the product of the long diameter and the short diameter of the area, and the field also has two sets of rectangular features with two sets of opposite sides approximately equal to each other; while the wetland area in the remote sensing image has an irregular shape, and its pixel area and the product of the long and short diameters may be equal or unequal, but its opposite sides cannot be approximately equal to each other.

[0118] Based on this, in some embodiments, the following formula model can be constructed to further determine the field area according to the shape characteristics of each suspected field area:

[0119]

[0120] Among them, X v A represents the shape performance of the suspected field area v (i.e., the matching degree between the suspected field area and the rectangular shape feature); v Represents the total number of pixels in the suspected field area v; c represents the major axis of the suspected field area v; d represents the minor axis of the suspected field area v; b1, b2, b3, b4 respectively represent the pixel lengths of the edge of the suspected field area v; exp represents an exponential function with the natural constant e as the base. Its specific function can be referred to above and will not be repeated here.

[0121] It should be noted that, in this specification, the long diameter of the suspected field region v may refer to the length of a straight line segment passing through the geometric center of the suspected field region v and connecting the two farthest endpoints. Similarly, the short diameter of the suspected field region v may refer to the length of a straight line segment passing through the geometric center of the suspected field region v and connecting the two closest endpoints. b1, b2, b3, and b4 respectively represent the pixel lengths of the edge of the suspected field region v, wherein the edge corresponding to b1 is opposite to the edge corresponding to b3, and the edge corresponding to b2 is opposite to the edge corresponding to b4. In some embodiments, the four edges corresponding to b1, b2, b3, and b4 may be determined based on the directions of the long diameter and the short diameter. For example, in some embodiments, a maximum inscribed rectangle (or parallelogram) can be determined within the suspected field area v, and two sides of the rectangle (or parallelogram) are parallel to their corresponding major diameters, and the other two sides are parallel to their corresponding minor diameters, so that based on the intersection of the maximum inscribed rectangle (or parallelogram) and the suspected field area v, four sides corresponding to b1, b2, b3, and b4 are determined (the edge line between two adjacent intersection points is regarded as a side of the suspected field area v).

[0122] It should also be noted that in the embodiments of this specification, represents the area similarity between the suspected field region v and the rectangle. The closer this value is to 1, the closer the suspected field region v is to a rectangle in terms of area characteristics. |b1-b3| and |b2-b4| represent the pixel length difference between the two opposite edges of the suspected field region v. The closer this difference is to 0, the closer the suspected field region v is to a rectangle. exp(-|b1-b3|) and exp(-| b 2-b4|) represents the similarity between the suspected field region v and the side length of the rectangle. The closer this value is to 1, the closer the suspected field region v is to the rectangle in terms of side length. The closer this value is to 0, the less the suspected field region v is like a rectangle in terms of side length. v The closer it is to 1, the higher the possibility that the suspected field area v belongs to the field area; the shape expression X v The closer it is to 0, the lower the possibility that the suspected field area v belongs to the field area.

[0123] Specifically, in some embodiments, the feature detection module 240 can obtain the major diameter c, minor diameter d, and number of pixels A corresponding to the suspected field area. v And the pixel length of the edge of the area (b1, b2, b3, b4), then based on the long diameter c, short diameter d and number of pixels A corresponding to the suspected field area v , calculate the area similarity between the suspected field area and the rectangle Furthermore, the feature detection module 240 can calculate the pixel length differences of the relative edges |b1-b3| and |b2-b4| based on the pixel lengths (b1, b2, b3, b4) of the area edges corresponding to the suspected field area; finally, the feature detection module 240 can determine the shape representation of the suspected field area based on the aforementioned area similarity and the pixel length differences of the relative edges. Exemplarily, in some embodiments, the feature detection module 240 can normalize the aforementioned pixel length differences of the relative edges, and use the product of the normalization result (exp(-|b1-b3|) and exp(-|b2-b4|)) and the aforementioned area similarity as the shape representation of the suspected field area.

[0124] Furthermore, after the shape representation of each suspected field area is obtained through the above steps, the feature detection module 240 can detect the field area based on the shape representation X. v The suspected field area is divided into field area, question area and non-field area. v The suspected field area smaller than the second preset threshold indicates that the possibility of it belonging to the field area is extremely small, and the feature detection module 240 can classify it as a non-field area; similarly, for the shape representation X v The suspected field area greater than the third preset threshold value indicates that it is very likely to belong to the field area, and the feature detection module 240 can classify it as a field area; v A suspected field region that is greater than or equal to the second preset threshold and less than or equal to the third preset threshold indicates that it may belong to a field region or a non-field region. The feature detection module 240 may classify it as a questionable region and further analyze it in subsequent steps to determine whether it belongs to a field region or a non-field region. In some embodiments of the present specification, the second preset threshold may be 0.3 and the third preset threshold may be 0.7.

[0125] In some embodiments, the feature detection module 240 may determine whether each questionable region belongs to a field region or a non-field region based on the feature performance of each questionable region relative to surrounding adjacent regions.

[0126] For example, in some embodiments, the characteristic representation of the question area can be calculated based on the following formula:

[0127]

[0128] Among them, Q O Indicates the characteristic performance of the question area O; l m represents the distance from the center of the adjacent region m (adjacent to the question region O) to the center of the question region O; represents the average value of the distances from the center of each adjacent region (adjacent to the question region O) to the center of the question region O; m represents the shape performance of the adjacent region m (adjacent to the query region O) (which can be calculated by the calculation method of the shape performance described above), min(X O,t ) represents the minimum value of the shape of the query area O and each adjacent area, max(X O,t ) represents the maximum value of the shape performance of the question area O and each adjacent area; k represents the total number of adjacent areas corresponding to the question area O; exp represents an exponential function with the natural constant e as the base, and its specific role can be referred to above and will not be repeated here.

[0129] It should be noted that in this specification, the center of each area mentioned above may refer to the geometric center. In the above formula, In some embodiments, for the neighboring region m adjacent to the question region O, the feature detection module 240 may calculate the distance l from the center of the neighboring region m to the center of the question region O. m The average value of the distance from the center of each adjacent area to the center of the question area O Ratio Then the ratio As the distance expression of the neighboring region m (adjacent to the question region O) relative to other neighboring regions. The smaller the distance expression, the closer the neighboring region m (adjacent to the question region O) is to the question region O, and the higher the possibility that they belong to the same region.

[0130] In the above formula, represents the difference in shape performance of the neighboring region m (adjacent to the question region O) relative to all neighboring regions around the question region O. Specifically, in some embodiments, for the neighboring region m adjacent to the question region O, the feature detection module 240 may calculate the shape performance X of the neighboring region m. m The minimum value min(X) of the shape expression corresponding to all adjacent regions adjacent to the query region O O,t ) of the first difference (X m -min(X O,t )), and the shape representation maximum value max(X O,t ) and the minimum value min(X O,t )'s second difference (max(X O,t )-min(H O,t )), and the ratio of the first difference to the second difference As the shape expression difference of the adjacent region m. The smaller the shape expression difference, the more similar the shape of the adjacent region m is to the shape of the question region O, and the greater the possibility that the adjacent region m and the question region O belong to the same region; the larger the shape expression difference, the less similar the shape of the adjacent region m and the question region O are, and the smaller the possibility that the adjacent region m and the question region O belong to the same region.

[0131] Specifically, in some embodiments of the present specification, the feature detection module 240 may identify the surrounding area of ​​the question area in the following manner, and determine whether the question area is a field area or a non-field area based on the situation of the adjacent areas around the question area:

[0132] First, the feature detection module 240 may determine the surrounding area of ​​the question area. In this specification, the surrounding area of ​​the question area of ​​the question area may refer to an area whose minimum distance to the question area is less than a preset threshold (eg, 20 pixels).

[0133] Then, for each question area, the feature detection module 240 can obtain the distance performance of each adjacent area relative to other adjacent areas based on the distance from the center of each adjacent area to the center of the question area and the average of the distances from the center of each adjacent area to the center of the question area.

[0134] Furthermore, the feature detection module 240 can obtain the difference in shape performance of each adjacent area relative to the surrounding adjacent areas of the question area based on the shape performance of each adjacent area and the maximum and minimum values ​​of the shape performance corresponding to all adjacent areas adjacent to the question area.

[0135] Furthermore, the feature detection module 240 can obtain the normalized feature value (i.e., ).

[0136] Furthermore, the feature detection module 240 may calculate the mean of the normalized feature values ​​corresponding to all adjacent regions adjacent to the question region: Get the characteristic expression Q of the question area O The characteristic representation of the question area Q O The larger it is, the more fields there are around the question area O, and the greater the possibility that the question area O belongs to a field.

[0137] Finally, the feature detection module 240 may mark the question area whose feature performance is greater than or equal to a fourth preset threshold as a field area, and mark the question area whose feature performance is less than the fourth preset threshold as a non-field area. In some embodiments, the fourth preset threshold may be 0.5.

[0138] Through the above steps, all questionable areas can be analyzed to determine whether they belong to field areas or non-field areas. After completing the analysis and processing of all questionable areas, all areas that may belong to fields in the remote sensing image corresponding to the target area can be obtained. Further, the feature detection module 240 can exclude the field area identified by the above process from the remote sensing image corresponding to the target area to obtain a third image to be processed.

[0139] Step 350 , classify based on the third image to be processed to obtain a wetland area. In some embodiments, step 350 may be performed by the classification module 250 .

[0140] In some embodiments, the classification module 250 may classify the third image to be processed that excludes field interference using an iterative self-organizing clustering method, thereby classifying various ground object elements in the remote sensing image corresponding to the target area to determine the wetland area in the remote sensing image. The specific process of determining the wetland area from the remote sensing image using the iterative self-organizing clustering method can be regarded as a prior art and will not be discussed in detail in this specification.

[0141] It can be understood that in the present specification, by excluding the field areas in the remote sensing images through the above steps, it is possible to avoid including the field areas in the wetland scope during the classification process using the iterative self-organizing clustering method, thereby improving the accuracy of the wetland scope identification results and reducing the interference of the field areas on the identification of the wetland restoration status.

[0142] Step 360 , determining the wetland restoration status of the target area according to the range change of the wetland area. In some embodiments, step 360 may be performed by the wetland restoration status determination module 260 .

[0143] Specifically, in some embodiments, the wetland restoration status determination module 260 can determine the wetland restoration status of the target area by comparing the wetland area ranges in the remote sensing images collected at different time points. For example, the wetland area range obtained by the above process can be compared with the reference range or the wetland range obtained by the last identification.

[0144] Specifically, in some embodiments, an acquisition cycle can be set (for example, it can be set to one week), and then a remote sensing image corresponding to the target area is acquired once through GF-2 in each acquisition cycle, and a binary image of the wetland area in the remote sensing images corresponding to several acquisition cycles is acquired according to the above method (for example, the wetland area determined by the above method is converted to black, and the remaining objects are converted to white).

[0145] Furthermore, the wetland restoration status determination module 260 can determine the area of ​​the actual remote sensing area corresponding to a pixel in the remote sensing image according to the internal and external parameters of GF-2 (such as resolution, orbital height, etc.), and then calculate the actual area of ​​the wetland area in each period by combining the binary image of the wetland area obtained for each period and the area of ​​the remote sensing area corresponding to each image pixel. Finally, by comparing the changes in the wetland area of ​​the current period with the wetland area of ​​the previous periods, the wetland restoration status of the target area can be determined. If the wetland range in the remote sensing image continues to increase, it means that the wetland restoration effect is good. On the contrary, if the wetland range in the remote sensing image continues to decrease, it means that the wetland is still deteriorating and the protection of the wetland ecological environment needs to be further strengthened.

[0146] In summary, the beneficial effects that may be brought about by the embodiments of this specification include but are not limited to: (1) In the wetland restoration status identification method and system based on remote sensing technology provided in some embodiments of this specification, by excluding the field area in the remote sensing image and then performing wetland identification on the remaining image, the interference of the field in the remote sensing image on the wetland classification can be effectively reduced, thereby improving the accuracy of wetland restoration status identification; (2) In the wetland restoration status identification method and system based on remote sensing technology provided in some embodiments of this specification, by identifying the field area in the remote sensing image based on color features, shape features and the conditions of the surrounding adjacent areas, the field area in the remote sensing image can be accurately and effectively identified.

[0147] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or may be any other beneficial effects that may be obtained.

[0148] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0149] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0150] In addition, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of this specification may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of this specification may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0151] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0152] The computer program code required for the operation of each part of this specification can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0153] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing processing device or mobile device.

[0154] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0155] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0156] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0157] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for identifying wetland restoration status based on remote sensing technology, characterized in that: include: Acquire a remote sensing image corresponding to the target area, wherein the remote sensing image is acquired within a specified time period; Preprocessing the remote sensing image to obtain a first image to be processed; Performing edge detection on the first image to be processed, and recording each closed area formed by an edge as a detected area, to obtain a second image to be processed; Performing feature detection on the second image to be processed, identifying a field area in the remote sensing image, and excluding the field area from the remote sensing image to obtain a third image to be processed; Performing classification based on the third image to be processed to obtain a wetland area; The wetland restoration status of the target area is determined based on changes in the scope of the wetland area.

2. The wetland restoration status identification method based on remote sensing technology according to claim 1, characterized in that: The performing feature detection on the second image to be processed to identify the field area in the remote sensing image includes: Detecting the color characteristics of the area to be tested to obtain a suspected field area; According to the edge shape characteristics of the suspected field area, the suspected field area is divided into a field area, a questionable area and a non-field area; The surrounding area of ​​the question area is identified, and it is determined whether the question area belongs to a field area or a non-field area based on the situation of the adjacent areas around the question area.

3. The wetland restoration status identification method based on remote sensing technology according to claim 2, characterized in that: The detecting the color feature of the area to be detected to obtain the suspected field area includes: For each area to be tested, the total number of pixels in the area to be tested and the grayscale value of each pixel are obtained; Calculating the similarity of each pixel in the area to be tested to the overall color of the area to be tested; Based on the similarity, calculating the color consistency within the tested area; The area to be tested whose color consistency is greater than a first preset threshold is marked as a suspected field area.

4. The wetland restoration status identification method based on remote sensing technology according to claim 3 is characterized in that: The similarity of each pixel point in the area to be tested to the overall color of the area to be tested, and the color consistency in the area to be tested are obtained based on the following method: Calculate the average gray value in the area to be tested; Obtaining a first parameter based on a ratio of a grayscale value of each pixel in the area to be measured to the average grayscale value; Normalizing the first parameter to obtain the similarity of each pixel point to the overall color of the area to be measured; The average value of the similarity of all pixels in the area to be tested relative to the overall color of the area to be tested is calculated to obtain the color consistency in the area to be tested.

5. The wetland restoration status identification method based on remote sensing technology according to claim 4, characterized in that: The method of dividing the suspected field area into a field area, a questionable area and a non-field area according to the edge shape characteristics of the suspected field area includes: Obtaining the major diameter, minor diameter, number of pixels and pixel length of the edge of the suspected field area; Based on the major diameter, minor diameter and number of pixels corresponding to the suspected field area, calculating the area similarity between the suspected field area and the rectangle; Calculating the pixel length difference of the relative edge based on the pixel length of the edge of the area corresponding to the suspected field area; Determining the shape representation of the suspected field area according to the area similarity and the pixel length difference of the relative edges; The suspected field area whose shape performance is less than the second preset threshold is classified as a non-field area; the suspected field area whose shape performance is greater than the third preset threshold is classified as a field area; the suspected field area whose shape performance is greater than or equal to the second preset threshold and less than or equal to the third preset threshold is classified as a questionable area.

6. The wetland restoration status identification method based on remote sensing technology according to claim 5, characterized in that: The determining of the shape of the suspected field area according to the area similarity and the pixel length difference of the relative edges includes: The pixel length differences of the relative edges are normalized, and the product of the normalization result and the area similarity is used as the shape representation of the suspected field area.

7. The wetland restoration status identification method based on remote sensing technology according to claim 5, characterized in that: The step of identifying the surrounding area of ​​the question area and determining whether the question area is a field area or a non-field area based on the situation of the adjacent areas around the question area includes: Based on the distance from the center of each adjacent region to the center of the question region and the average of the distances from the center of each adjacent region to the center of the question region, the distance performance of each adjacent region relative to other adjacent regions is obtained; Based on the shape performance of each adjacent region and the maximum and minimum values ​​of the shape performances corresponding to all adjacent regions adjacent to the question region, the shape performance difference of each adjacent region relative to the surrounding adjacent regions of the question region is obtained; Based on the distance performance of each adjacent region relative to other adjacent regions and the difference in shape performance of each adjacent region relative to the surrounding adjacent regions of the question region, a normalized feature value corresponding to each adjacent region is obtained; Calculate the average of normalized feature values ​​corresponding to all adjacent regions adjacent to the question region to obtain feature representation of the question region; The question area whose characteristic performance is greater than or equal to a fourth preset threshold is marked as a field area, and the question area whose characteristic performance is less than the fourth preset threshold is marked as a non-field area.

8. The wetland restoration status identification method based on remote sensing technology according to claim 7, characterized in that: The distance representation of the adjacent region relative to other adjacent regions is obtained based on the following method: For the adjacent region m adjacent to the question region O, calculate the distance l from the center of the adjacent region m to the center of the question region O m The average value of the distance from the center of each adjacent area to the center of the question area O Ratio The ratio It is expressed as the distance between the adjacent area m and other adjacent areas.

9. The wetland restoration status identification method based on remote sensing technology according to claim 7, characterized in that: The difference in shape performance between the adjacent region and the surrounding adjacent region of the question region is obtained based on the following method: For the adjacent region m adjacent to the question region O, calculate the shape representation X of the adjacent region m m The minimum value min(X) of the shape expression corresponding to all adjacent regions adjacent to the query region O O,t ), and the shape representation maximum value max(X O,t ) and the minimum value min(X O,t )’s second difference; The ratio of the first difference to the second difference is used as the shape representation difference of the adjacent region m.

10. A wetland restoration status identification system based on remote sensing technology, characterized in that: include: An acquisition module is used to acquire a remote sensing image corresponding to a target area, wherein the remote sensing image is acquired within a specified time period; A preprocessing module, used for preprocessing the remote sensing image to obtain a first image to be processed; An edge detection module is used to perform edge detection on the first image to be processed, and record each closed area formed by an edge as a detected area, so as to obtain a second image to be processed; a feature detection module, configured to perform feature detection on the second image to be processed, identify a field area in the remote sensing image, and exclude the field area from the remote sensing image to obtain a third image to be processed; A classification module, used for performing classification based on the third image to be processed to obtain a wetland area; The wetland restoration status determination module is used to determine the wetland restoration status of the target area according to the range change of the wetland area.

Images