Reservoir water body boundary determination method and device and processing equipment

Abstract

The invention provides a reservoir water body boundary determination method, a reservoir water body boundary determination device and processing equipment, which are used for finely determining a dynamic range of a reservoir water body on the basis of an improved normalized water body index and an Otsu method, and compared with a single time phase water body extraction method, an outer boundary region with the widest geographic position and the longest time range can be captured, so that the extraction efficiency is improved. Compared with a long-time-sequence water body extraction method, the method can eliminate the influence that the water body is exposed out of a hydro-fluctuation zone and a bank zone, and a more accurate inner boundary region is captured, so that the high-quality recognition requirement of a complex reservoir water body can be well met.

Description

Technical Field

[0001] The present application relates to the field of image analysis, and specifically to a method, device and processing equipment for determining the boundary of a reservoir water body. Background Art

[0002] Accurately identifying reservoir water bodies through satellite remote sensing technology can provide data support for monitoring hydrological conditions, which is obviously of great practical significance for hydrological management.

[0003] At present, the mainstream water body identification strategies are: 1) Water body identification based on a single time phase, mostly using NDWI, MNDWI, AWEI and other water body index sets to manually select thresholds to extract the water body range. 2) Combine long-term multispectral or radar images of one or more years to classify permanent water bodies and seasonal water bodies.

[0004] In the former, the water body identification in a single time phase can meet the time frequency requirements of general reservoir water area monitoring, but it is easy to cause misclassification of water bodies and other land types, and the identification accuracy and reliability are low; in the latter, the water body identification accuracy is higher, but it is not representative enough in a single time phase, and it is easy to cause missed classification and over-classification of water bodies and drawdown zones.

[0005] Therefore, the above two types of water body identification strategies are more suitable for water body identification in lakes and plain rivers. They have high identification accuracy, high computational efficiency, and meet the requirements of time resolution, but they are difficult to meet the application needs of water body identification in complex reservoirs. Summary of the invention

[0006] The present application provides a reservoir water body boundary determination method, device and processing equipment, which are used to finely determine the dynamic range of the reservoir water body based on an improved normalized water body index and combined with the Otsu method. Compared with the single-phase water body extraction method, it can capture the outer boundary area with the widest geographical location and the longest time range. Compared with the long-time series water body extraction method, it can exclude the influence of water body leakage out of the drawdown zone and the shore zone, and capture a more accurate inner boundary area, which can well meet the high-quality identification requirements for complex reservoir water bodies.

[0007] In a first aspect, the present application provides a method for determining a reservoir water body boundary, the method comprising: Acquire an initial multispectral satellite image within the target area, wherein the initial multispectral satellite image has a time series attribute; Perform data enhancement operation on the initial multispectral satellite image to obtain the target multispectral satellite image; Based on the improved normalized water index calculated by time series of the target multispectral satellite image, the index threshold of the improved normalized water index is calculated by combining the Otsu method, and the preliminary water pixels of all time phases are screened out to obtain the preliminary water pixel screening results. Based on the preliminary water body pixel screening results, the reservoir constant water body and the reservoir temporal water body are calculated respectively to obtain the reservoir water body boundary range with the reservoir constant water body as the outer boundary and the reservoir temporal water body as the inner boundary.

[0008] In a second aspect, the present application provides a device for determining a reservoir water body boundary, the device comprising: An acquisition unit is used to acquire an initial multispectral satellite image in a target area, wherein the initial multispectral satellite image has a time series attribute; An operation unit, used for performing data enhancement operation on the initial multispectral satellite image to obtain a target multispectral satellite image; A screening unit is used to calculate the index threshold of the improved normalized water index based on the improved normalized water index calculated by the target multispectral satellite image in time series, and screen out preliminary water pixels of all time phases to obtain preliminary water pixel screening results; The calculation unit is used to calculate the constant water body of the reservoir and the temporal water body of the reservoir respectively based on the preliminary water body pixel screening results, and obtain the boundary range of the reservoir water body with the constant water body of the reservoir as the outer boundary and the temporal water body of the reservoir as the inner boundary.

[0009] In a third aspect, the present application provides a processing device, including a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method provided in the first aspect of the present application or any possible implementation method of the first aspect of the present application is executed.

[0010] In a fourth aspect, the present application provides a computer-readable storage medium, which stores multiple instructions, and the instructions are suitable for a processor to load to execute the method provided by the first aspect of the present application or any possible implementation of the first aspect of the present application.

[0011] From the above content, it can be concluded that the present application has the following beneficial effects: Under the goal of reservoir water body identification based on satellite remote sensing images, this application is based on the improved normalized water body index and the Otsu method to finely determine the dynamic range of reservoir water bodies. Compared with the single-phase water body extraction method, it can capture the outer boundary area with the widest geographical location and the longest time range. Compared with the long-time series water body extraction method, it can exclude the influence of water bodies leaking out of the drawdown zone and the shore zone, and capture more accurate inner boundary areas. This can well meet the high-quality identification needs of complex reservoir water bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of a flow chart of a method for determining the water body boundary of a reservoir in this application; Figure 2 A schematic diagram of the structure of a device for determining the boundary of a reservoir water body in this application; Figure 3 A schematic diagram of the structure of the processing equipment of this application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0015] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0016] The division of modules in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. In addition, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0017] Before introducing the reservoir water body boundary determination method provided in this application, the background content involved in this application is first introduced.

[0018] The reservoir water body boundary determination method, device and computer-readable storage medium provided in the present application can be applied to processing equipment to finely determine the dynamic range of the reservoir water body based on the improved normalized water body index and the Otsu method. Compared with the single-phase water body extraction method, it can capture the outer boundary area with the widest geographical location and the longest time range. Compared with the long-time series water body extraction method, it can exclude the influence of water body leakage out of the drawdown zone and the shore zone, and capture a more accurate inner boundary area, which can well meet the high-quality identification requirements for complex reservoir water bodies.

[0019] The reservoir water body boundary determination method mentioned in this application can be executed by a reservoir water body boundary determination device, or a server, physical host or user equipment (UE) and other different types of processing equipment integrated with the reservoir water body boundary determination device. Among them, the reservoir water body boundary determination device can be implemented in hardware or software, and the UE can be a terminal device such as a smart phone, tablet computer, laptop computer, desktop computer or personal digital assistant (PDA), and the processing device can be set in the form of a device cluster.

[0020] It can be understood that in actual applications, the present application scheme usually carries out data processing (which can also be said to be image detection and image recognition) based on existing satellite remote sensing images. Therefore, the processing equipment that executes the reservoir water body boundary determination method of the present application or is equipped with the corresponding application service of the reservoir water body boundary determination method of the present application only needs to meet the data processing capability requirements. The specific equipment type and equipment deployment form are relatively flexible and can be adaptively adjusted according to actual conditions.

[0021] If it involves the specific collection of satellite remote sensing images or further data application based on a certain reservoir water body, further adaptive adjustments will be needed to the equipment type and equipment deployment form of the processing equipment.

[0022] As an example, a satellite remote sensing image acquisition system can also be included in the scope of the processing device of the present application, so that the processing device itself has the satellite remote sensing image acquisition function.

[0023] Next, we will introduce the reservoir water body boundary determination method provided by this application.

[0024] First, see Figure 1 , Figure 1 A schematic flow chart of the reservoir water body boundary determination method provided in the present application is shown. The reservoir water body boundary determination method provided in the present application may specifically include the following steps S101 to S104: Step S101, obtaining an initial multispectral satellite image within a target area, wherein the initial multispectral satellite image has a time series attribute; It can be understood that the present application is similar to the prior art in that it is based on satellite remote sensing images to identify the boundaries of reservoir water bodies contained in the selected target area (usually the existence of reservoir water bodies is known, and in some cases attempts may be made to search for reservoir water bodies, i.e. unknown reservoir water bodies).

[0025] Among them, this application specifically develops the solution based on multispectral satellite images. Multispectral satellite images are a relatively mature concept / thing in satellite remote sensing technology, so no specific explanation is given here. For the convenience of explanation, this application records the multispectral satellite images obtained here as initial multispectral satellite images, and the same applies to subsequent target multispectral satellite images.

[0026] It should be noted that the initial multispectral satellite image obtained here has time series attributes, or is a multispectral satellite image with a long time series (time series length / time span is greater than the corresponding threshold).

[0027] In specific operations, the acquisition of the initial multispectral satellite image here can be either manual entry, reading from local or other devices, or real-time collection, and the specific adjustments will be made according to the actual situation.

[0028] Further, as an exemplary embodiment here, step S101 of acquiring an initial multispectral satellite image in the target area may specifically include: For the target area, determine the target satellite telemetry product that obtains the best evaluation value in terms of image quality and cloud cover from among the candidate different satellite telemetry products; Obtain the initial multispectral satellite image of the target satellite telemetry product in the target area.

[0029] It can be understood that in practical applications, the present application can specifically and conveniently obtain the initial multispectral satellite images required for solution processing from existing satellite telemetry products (or satellite remote sensing products), and in this process, it can also involve the selection of specific satellite telemetry products, that is, target satellite telemetry products, so that the boundaries of the reservoir water bodies in the target area can be more conveniently and accurately identified based on higher quality initial multispectral satellite images.

[0030] In the selection stage, the quantification of the evaluation value may be specifically involved. The evaluation value may be quantified based on a series of preset considerations. For example, it may be quantified by a weighted method. The considerations involved may include specific factors such as image quality and cloud cover. Obviously, the higher the image quality and the less cloud cover, the higher the data quality.

[0031] As an example, the target satellite telemetry product selected or determined in the present application may specifically be the L1C multispectral image of Sentinel-2 MSI.

[0032] Step S102, performing data enhancement operation on the initial multispectral satellite image to obtain a target multispectral satellite image; After obtaining the initial multispectral satellite imagery, this application does not directly carry out the identification and processing of the reservoir water body boundary, but instead performs data enhancement operations / processing on it to continue to improve the image quality through secondary processing of the data, thereby laying a better foundation for the subsequent identification and processing of the reservoir water body boundary.

[0033] Among them, it can be understood that for the data enhancement operations that can be taken in specific operations, you can either adopt the existing solutions (there are a large number of relatively mature and common specific operation methods for data enhancement operations in the existing technology), or make further optimization and improvements on the basis of the existing solutions, or adopt novel self-developed solutions. These are all possible and can be adjusted according to actual needs.

[0034] Further, as an exemplary embodiment here, step S102 performs a data enhancement operation on the initial multispectral satellite image to obtain a target multispectral satellite image, which may specifically include: Perform preprocessing operations on the initial multispectral satellite image, where the preprocessing operations include radiometric calibration, cloud removal, mosaicking, and cropping; The quality control band is used to further remove the pixels of clouds, cloud shadows, cloudy and cirrus clouds from the image obtained by the preprocessing operation to obtain the target multispectral satellite image.

[0035] Among them, radiometric calibration, cloud removal, mosaicking and cropping are several types of operations commonly used for data enhancement and data preprocessing of satellite remote sensing images. For example, the data preprocessing here can be achieved through the Sen2cor plug-in in SNAP, and the quality control band can also be understood by the band value setting strategy for effectively classifying / differentiating cloud attribute pixels such as cloud layer, cloud shadow, cloudy and cirrus. In some cases, it can also be a ready-made application service, such as the quality_scene_classification component of the SNAP product.

[0036] Thus, in this embodiment, while obtaining standardized, high-quality image data through the general data enhancement means involved in the first stage, the quality of data is further effectively improved through the quality control bands specially designed for cloud attribute pixels in the second stage.

[0037] Step S103, based on the improved normalized water index calculated by the target multispectral satellite image in time series, the index threshold of the improved normalized water index is calculated by combining the Otsu method, and preliminary water pixels of all time phases are screened to obtain preliminary water pixel screening results; It can be understood that after obtaining the target multispectral satellite image with significantly enhanced quality, specific reservoir water body identification processing can be carried out.

[0038] Specifically, this application involves two major stages of reservoir water body identification and processing. The first stage is to perform preliminary identification of water body pixels, and the subsequent second stage is to perform more delicate processing in terms of details, so as to achieve accurate capture of the reservoir water body boundary.

[0039] In the first stage, this application combines the Modified Normalized Difference Water Index (MNDWI) and the OTSU method (also known as the maximum inter-class variance method) to perform preliminary division and screening of water pixels from pixels of different phases (corresponding to long time series characteristics) involved in the target multispectral satellite image.

[0040] Among them, both of them belong to the scope of existing technologies / algorithms. Considering that they are not the focus of this application, they will not be explained in detail here.

[0041] Combined with the index threshold calculated by the Otsu method, it can be understood that in the specific calculation process, it is carried out in a phase-by-phase manner, which corresponds to the subsequent processing of screening preliminary water body pixels for each phase.

[0042] In specific operations, for the current phase, the corresponding index threshold can be recorded as K. In the current phase, if the improved normalized water index calculated for a certain pixel is greater than the index threshold, it can be identified / marked as a water pixel, that is, the water pixel belongs to the reservoir water body range, otherwise it is identified / marked as a non-water pixel.

[0043] Corresponding to the calculation of the improved normalized water index, as a specific embodiment, the method of the present application may further include: According to the following formula, the improved normalized water index is calculated for the target multispectral satellite image in time series: , in, is the value of the improved normalized water index, is the reflectance value of the green band (560nm), It is the reflectivity value in the short-wave infrared band (1610nm).

[0044] Step S104, based on the preliminary water body pixel screening results, respectively calculate the reservoir constant water body and the reservoir temporal water body to obtain the reservoir water body boundary range with the reservoir constant water body as the outer boundary and the reservoir temporal water body as the inner boundary.

[0045] Based on the previous combination of the improved normalized water index and the Otsu method to determine whether each pixel in each time phase is a water body pixel or a non-water body pixel, the constant water body of the reservoir and the reservoir phase water body involved in this application scheme can be calculated separately based on the classification result of the two categories, that is, the preliminary water body pixel screening result.

[0046] Among them, the constant water body of the reservoir can be considered as a relatively fixed reservoir water body under the actual conditions of the current reservoir in its area, and the temporal water body of the reservoir can be considered as a reservoir water body that dynamically changes with time under the actual conditions of the current reservoir in its area.

[0047] For the former, this application uses it as the outer boundary of the dynamic reservoir water body range, and for the latter, this application uses it as the inner boundary of the dynamic reservoir water body range. From the visualization point of view, by superimposing the layer of the reservoir phase water body on the layer of the reservoir constant water body, the dynamic reservoir water body range boundary of the current complex reservoir can be obtained.

[0048] Further, for the calculation and processing of the constant water body of the reservoir, as an exemplary embodiment, the present application calculates the constant water body of the reservoir based on the preliminary water body pixel screening results, which may specifically include: Based on the preliminary water pixel screening results, the following formula is used to perform pixel-by-pixel iterative statistics on the preliminary water pixels of all time phases to obtain the total waterlogging probability value of each pixel: , Among them, WF is the value of the total waterlogging probability value, M is the total number of valid observations within the time range involved in the initial multispectral satellite image, and T is a binary variable, T=1 under water body conditions and T=0 under non-water body conditions; The set of different pixels whose total waterlogging probability values ​​are greater than the total waterlogging probability threshold is determined as the constant water body of the reservoir.

[0049] It can be seen that the embodiment here is how to calculate the constant water body of the reservoir in this application. Starting from the quantitative formula level, a set of specific implementation solutions is given, which has better practical value. It can also be seen that the embodiment here considers all phases within the time range (time span) involved in the initial multispectral satellite image / target multispectral satellite image from an overall level.

[0050] As for the total water accumulation probability threshold involved, it can be understood that in actual operation it can be adaptively adjusted according to actual conditions and actual needs, and it can also be understood by an empirical coefficient. Moreover, for different reservoirs, different total water accumulation probability thresholds may need to be configured.

[0051] Specifically, the total waterlogging probability threshold can be determined according to the proportion of the flood season in the complex reservoir basin to the annual time. The flood season is the proportion of the period when the water area of ​​the complex reservoir basin expands, and the dry season is the proportion of the period when the water area of ​​the complex reservoir basin shrinks.

[0052] As an example, the total waterlogging probability threshold may be 70%.

[0053] On the other hand, for the calculation and processing of the reservoir phase water body, as an exemplary embodiment, the present application calculates the reservoir phase water body based on the preliminary water body pixel screening result, which may specifically include: On the basis of the preliminary water body pixel screening results, combined with the constraints of the maximum number of connected domains to be eliminated and the maximum number of pixels to be eliminated, for the water body area composed of preliminary water body pixels in each phase, the connected domains whose size is less than the connected domain size threshold are removed to obtain the reservoir phase water body.

[0054] It can be seen that in the embodiment here, the unit phase within the time range (time span) involved in the initial multispectral satellite image / target multispectral satellite image is considered specifically from the local level, and on the basis of the connected domain processing combined with the corresponding constraints, all connected domains are further simplified to obtain the precise connected domain of the corresponding phase water body in each phase.

[0055] Among them, the constraints can be specifically configured from aspects such as the maximum number of connected domains to be eliminated and the maximum number of pixels to be eliminated, so as to eliminate the influence of buildings and paddy fields and obtain a more concise and accurate connected domain processing effect.

[0056] In addition, it is understandable that the maximum number of connected domains to be eliminated and the maximum number of pixels to be eliminated can also be adjusted according to actual conditions and actual needs in practical applications.

[0057] As an example, the maximum number of connected domains to be eliminated may be 8, and the maximum number of pixels to be eliminated may be 50.

[0058] In this way, the dynamic reservoir water body boundary (represented by different phases) determined based on the constant water body of the reservoir and the temporal water body of the reservoir, compared with the single-phase water body extraction method in the existing technology, the outer boundary of the dynamic water body range retains the water body boundary with the widest geographical location and the longest time range in the complex reservoir basin, and can keep the position relatively fixed to meet the needs of water quality inversion and algal bloom extraction remote sensing batch calculation; compared with the long-term water body extraction method in the existing technology, the inner boundary of the dynamic water body range excludes the influence of the water body drawdown in the complex reservoir basin during the dry season, which exposes the drawdown zone and the shore zone. This can provide strong data support for hydrological management work and its corresponding personnel.

[0059] In terms of application, this application may also continue to involve corresponding data application links.

[0060] Specifically, based on the preliminary water body pixel screening results, the constant water body of the reservoir and the reservoir phase water body are calculated respectively, and after obtaining the reservoir water body boundary range with the constant water body of the reservoir as the outer boundary and the reservoir phase water body as the inner boundary, the application method may also include: For the boundary range of the water body of the reservoir, at least one of the following data application processes is performed: local storage, remote storage, result display, output of prompts for completion of boundary identification, result forwarding, and further data analysis.

[0061] It can be understood that in actual applications, specific data application processing can be further adjusted according to the pre-configured and implemented data application policies / rules, and this application does not specifically limit this.

[0062] Finally, for the above solution content, overall, under the goal of reservoir water body identification based on satellite remote sensing images, this application is based on the improved normalized water body index and the Otsu method to finely determine the dynamic range of reservoir water bodies. Compared with the single-phase water body extraction method, it can capture the outer boundary area with the widest geographical location and the longest time range. Compared with the long-term water body extraction method, it can exclude the influence of water bodies leaking out of the drawdown zone and the shore zone, and capture more accurate inner boundary areas. This can well meet the high-quality identification needs of complex reservoir water bodies.

[0063] The above is an introduction to the reservoir water body boundary determination method provided in this application. In order to facilitate better implementation of the reservoir water body boundary determination method provided in this application, this application also provides a reservoir water body boundary determination device from the perspective of functional modules.

[0064] See also Figure 2 , Figure 2 This is a schematic diagram of a structure of a reservoir water body boundary determination device in this application. In this application, the reservoir water body boundary determination device 200 may specifically include the following structure: An acquisition unit 201 is used to acquire an initial multispectral satellite image in a target area, wherein the initial multispectral satellite image has a time series attribute; An operation unit 202 is used to perform a data enhancement operation on the initial multispectral satellite image to obtain a target multispectral satellite image; The screening unit 203 is used to screen out preliminary water body pixels of all time phases based on the improved normalized water body index calculated by the target multispectral satellite image in time series, and to obtain preliminary water body pixel screening results; The calculation unit 204 is used to calculate the constant water body of the reservoir and the temporal water body of the reservoir respectively based on the preliminary water body pixel screening results, and obtain the reservoir water body boundary range with the constant water body of the reservoir as the outer boundary and the temporal water body of the reservoir as the inner boundary.

[0065] In an exemplary embodiment, the acquisition unit 201 is specifically configured to: For the target area, determine the target satellite telemetry product that obtains the best evaluation value in terms of image quality and cloud cover from among the candidate different satellite telemetry products; Obtain the initial multispectral satellite image of the target satellite telemetry product in the target area.

[0066] In another exemplary embodiment, the operating unit 202 is specifically configured to: Performing preprocessing operations on the initial multispectral satellite image, wherein the preprocessing operations include radiometric calibration, cloud removal, mosaicking and clipping; The quality control band is used to further remove the pixels of clouds, cloud shadows, cloudy and cirrus clouds from the image obtained by the preprocessing operation to obtain the target multispectral satellite image.

[0067] In another exemplary embodiment, the computing unit 204 is further configured to: According to the following formula, the improved normalized water index is calculated for the target multispectral satellite image in time series: , in, is the value of the improved normalized water index, is the reflectance value of the green band, is the reflectance value in the shortwave infrared band.

[0068] In another exemplary embodiment, the calculation unit 204 is specifically configured to: Based on the preliminary water pixel screening results, the following formula is used to perform pixel-by-pixel iterative statistics on the preliminary water pixels of all time phases to obtain the total waterlogging probability value of each pixel: , Among them, WF is the value of the total waterlogging probability value, M is the total number of valid observations within the time range involved in the initial multispectral satellite image, and T is a binary variable, T=1 under water body conditions and T=0 under non-water body conditions; The set of different pixels whose total waterlogging probability values ​​are greater than the total waterlogging probability threshold is determined as the constant water body of the reservoir.

[0069] In another exemplary embodiment, the calculation unit 204 is specifically configured to: On the basis of the preliminary water body pixel screening results, combined with the constraints of the maximum number of connected domains to be eliminated and the maximum number of pixels to be eliminated, for the water body area composed of preliminary water body pixels in each phase, the connected domains whose size is less than the connected domain size threshold are removed to obtain the reservoir phase water body.

[0070] In yet another exemplary embodiment, the total water accumulation probability threshold is specifically 70%, the maximum number of connected domains to be eliminated is specifically 8, and the maximum number of pixels to be eliminated is specifically 50.

[0071] This application also provides a processing device from the perspective of hardware structure, see Figure 3 , Figure 3 301, a memory 302, and an input / output device 303. The processor 301 is used to execute the computer program stored in the memory 302 to implement the following Figure 1The steps of the reservoir water body boundary determination method in the corresponding embodiment; or, the processor 301 is used to execute the computer program stored in the memory 302 to implement the following Figure 2 Corresponding to the functions of each unit in the embodiment, the memory 302 is used to store the processor 301 executing the above Figure 1 The computer program required by the reservoir water body boundary determination method in the corresponding embodiment.

[0072] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 302, and executed by the processor 301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0073] The processing device may include, but is not limited to, a processor 301, a memory 302, and an input / output device 303. Those skilled in the art will appreciate that the illustration is merely an example of a processing device and does not constitute a limitation on the processing device, and may include more or fewer components than shown in the illustration, or a combination of certain components, or different components. For example, the processing device may also include a network access device, a bus, etc., and the processor 301, the memory 302, the input / output device 303, etc. are connected via a bus.

[0074] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the processing device, and uses various interfaces and lines to connect various parts of the entire device.

[0075] The memory 302 can be used to store computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the processing device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0076] When the processor 301 is used to execute the computer program stored in the memory 302, the following functions can be implemented: Acquire an initial multispectral satellite image within the target area, wherein the initial multispectral satellite image has a time series attribute; Perform data enhancement operation on the initial multispectral satellite image to obtain the target multispectral satellite image; Based on the improved normalized water index calculated by time series of the target multispectral satellite image, the index threshold of the improved normalized water index is calculated by combining the Otsu method, and the preliminary water pixels of all time phases are screened out to obtain the preliminary water pixel screening results. Based on the preliminary water body pixel screening results, the reservoir constant water body and the reservoir temporal water body are calculated respectively to obtain the reservoir water body boundary range with the reservoir constant water body as the outer boundary and the reservoir temporal water body as the inner boundary.

[0077] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the reservoir water body boundary determination device, processing equipment and corresponding units described above can refer to the following. Figure 1 The description of the method for determining the reservoir water body boundary in the corresponding embodiment will not be repeated here.

[0078] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0079] To this end, the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the present application as follows: Figure 1The steps of the reservoir water body boundary determination method in the corresponding embodiment, the specific operation can refer to the following Figure 1 The description of the reservoir water body boundary determination method in the corresponding embodiment will not be repeated here.

[0080] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0081] Due to the instructions stored in the computer-readable storage medium, the present application can be executed. Figure 1 The steps of the reservoir water body boundary determination method in the corresponding embodiment, therefore, the present application can be implemented as follows Figure 1 The beneficial effects that can be achieved by the reservoir water body boundary determination method in the corresponding embodiment are detailed in the previous description and will not be repeated here.

[0082] The above is a detailed introduction to the reservoir water body boundary determination method, device, processing equipment and computer-readable storage medium provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the core idea of ​​the present application; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for determining a reservoir water body boundary, characterized in that: The method comprises: Acquire an initial multispectral satellite image within the target area, wherein the initial multispectral satellite image has a time series attribute; Performing a data enhancement operation on the initial multispectral satellite image to obtain a target multispectral satellite image; Based on the improved normalized water index calculated by the target multispectral satellite image in time series, the index threshold of the improved normalized water index is calculated in combination with the Otsu method, and preliminary water pixels of all time phases are screened to obtain preliminary water pixel screening results; Based on the preliminary water body pixel screening results, the constant water body of the reservoir and the temporal water body of the reservoir are calculated respectively to obtain the boundary range of the reservoir water body with the constant water body of the reservoir as the outer boundary and the temporal water body of the reservoir as the inner boundary.

2. The method according to claim 1, characterized in that: The step of obtaining an initial multispectral satellite image within the target area includes: For the target area, determine the target satellite telemetry product that obtains the best evaluation value in terms of factors including image quality and cloud cover from among the candidate different satellite telemetry products; The initial multispectral satellite image of the target satellite telemetry product in the target area is obtained.

3. The method according to claim 1, characterized in that The performing of a data enhancement operation on the initial multispectral satellite image to obtain a target multispectral satellite image includes: Performing a preprocessing operation on the initial multispectral satellite image, wherein the preprocessing operation includes radiometric calibration, cloud removal, mosaicking, and clipping; The image obtained by the preprocessing operation is further subjected to the removal of pixels of clouds, cloud shadows, cloudy and cirrus clouds by using the quality control band to obtain the target multispectral satellite image.

4. The method according to claim 1, characterized in that: The method further comprises: The improved normalized water index is calculated for the target multispectral satellite image in time series according to the following formula: ， in, is the value of the improved normalized water index, is the reflectance value of the green band, is the reflectance value in the shortwave infrared band.

5. The method according to claim 1, characterized in that Based on the preliminary water body pixel screening results, the constant water body of the reservoir is calculated, including: Based on the preliminary water body pixel screening results, the preliminary water body pixels of all time phases are iteratively counted pixel by pixel by pixel using the following formula to obtain the total waterlogging probability value of each pixel: ， Wherein, WF is the value of the total waterlogging probability value, M is the total number of valid observations within the time range involved in the initial multispectral satellite image, T is a binary variable, T=1 under water body conditions, and T=0 under non-water body conditions; A set of different pixels whose total waterlogging probability values ​​are greater than the total waterlogging probability threshold is determined as the constant water body of the reservoir.

6. The method according to claim 5, characterized in that Based on the preliminary water body pixel screening results, the reservoir phase water body is calculated, including: On the basis of the preliminary water body pixel screening results, combined with the constraints of the maximum number of connected domains to be eliminated and the maximum number of pixels to be eliminated, for the water body area composed of the preliminary water body pixels in each phase, the connected domains whose size is smaller than the connected domain size threshold are removed to obtain the water body in the reservoir phase.

7. The method according to claim 6, characterized in that The total water accumulation probability threshold is specifically 70%, the maximum number of connected domains to be eliminated is specifically 8, and the maximum number of pixels to be eliminated is specifically 50.

8. A device for determining a reservoir water body boundary, characterized in that: The device comprises: An acquisition unit, used for acquiring an initial multispectral satellite image in a target area, wherein the initial multispectral satellite image has a time series attribute; An operation unit, configured to perform a data enhancement operation on the initial multispectral satellite image to obtain a target multispectral satellite image; A screening unit is used to calculate the index threshold of the improved normalized water index based on the improved normalized water index calculated by the target multispectral satellite image in time series, in combination with the Otsu method, to screen out preliminary water pixels of all time phases to obtain preliminary water pixel screening results; The calculation unit is used to calculate the constant water body of the reservoir and the temporal water body of the reservoir respectively based on the preliminary water body pixel screening results, and obtain the boundary range of the reservoir water body with the constant water body of the reservoir as the outer boundary and the temporal water body of the reservoir as the inner boundary.

9. A processing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the method according to any one of claims 1 to 7 when calling the computer program in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 7.

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