Big data driven remote sensing image recognition and classification method and system

Abstract

The invention discloses a big data driven remote sensing image recognition and classification method and system, and the method comprises the steps: obtaining a to-be-recognized region on a remote sensing image according to the remote sensing image, and extracting the image features and scale of the to-be-recognized region; setting a topographic condition recognition model, and calculating a comprehensive metric value of the topographic condition of the to-be-recognized region in the remote sensing image according to the image features and the scale of the to-be-recognized region; and comparing the comprehensive metric value of the topographic condition in the remote sensing image with a preset topographic threshold value, and if the comprehensive metric value of the topographic condition in the remote sensing image is greater than the preset topographic threshold value, classifying the corresponding to-be-identified region as a mountain.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing image recognition, and more specifically, relates to a big data driven remote sensing image recognition and classification method and system. Background Art

[0002] Remote sensing images are information about the Earth's surface or atmosphere obtained through remote sensing technology, usually collected by sensors on satellites or aircraft. Remote sensing images have a wide range of applications, including environmental monitoring, agriculture, urban planning, natural disaster assessment, etc.

[0003] Remote sensing images have a wide range of applications, the following are some of the main ones: Environmental monitoring: It is used to monitor environmental parameters such as forest cover changes, wetland status, vegetation index, etc., which helps in environmental protection and ecosystem management.

[0004] Agriculture: Helping farmers and agricultural experts monitor crop health, predict yields, and plan irrigation and fertilization to improve agricultural production efficiency and sustainability.

[0005] Urban planning: used for urban expansion monitoring, land use classification, infrastructure construction planning, etc., to support urban management and development.

[0006] Disaster Management: Providing real-time imagery before and after natural disasters (such as earthquakes, floods, fires, and hurricanes) to help assess impacts and develop rescue and recovery plans.

[0007] Climate change research: Analyze climate change trends through long-term remote sensing data series, monitor phenomena such as glacier retreat and sea level rise, and support the verification and improvement of climate models.

[0008] However, there is no technical solution in the prior art that can specifically identify the terrain conditions on remote sensing images. Summary of the invention

[0009] In order to solve the above technical problems, the present invention proposes a big data driven remote sensing image recognition and classification method, comprising: According to the remote sensing image, an area to be identified on the remote sensing image is obtained, and image features and scales of the area to be identified are extracted; Setting a terrain condition recognition model, and calculating a comprehensive measurement value of the terrain condition of the area to be identified in the remote sensing image according to the image features and scale of the area to be identified; The comprehensive measurement value of the terrain conditions in the remote sensing image is compared with a preset terrain threshold. If the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, the corresponding area to be identified is classified as mountains.

[0010] Furthermore, the terrain condition recognition model includes: , in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

[0011] Further, including: .

[0012] Furthermore, in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: , in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

[0013] Further, The weight of image features include: , in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, is the area adjacent to the area to be identified at position The gray value at .

[0014] The present invention also proposes a big data driven remote sensing image recognition and classification system, comprising: An information acquisition module is used to acquire the area to be identified on the remote sensing image according to the remote sensing image, and extract the image features and scale of the area to be identified; A model module is set up to set a terrain condition recognition model, and calculate a comprehensive measurement value of the terrain condition of the area to be recognized in the remote sensing image according to the image features and scale of the area to be recognized; The classification module is used to compare the comprehensive measurement value of the terrain conditions in the remote sensing image with a preset terrain threshold. If the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, the corresponding area to be identified is classified as mountains.

[0015] Furthermore, the terrain condition recognition model includes: , in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

[0016] Further, including: .

[0017] Furthermore, in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: , in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

[0018] Further, The weight of image features include: , in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, is the area adjacent to the area to be identified at position The gray value at .

[0019] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art: The present invention sets a terrain condition recognition model, and calculates the comprehensive measurement value of the terrain condition of the area to be recognized in the remote sensing image according to the image features and scale of the area to be recognized; the comprehensive measurement value of the terrain condition in the remote sensing image is compared with a preset terrain threshold, and if the comprehensive measurement value of the terrain condition in the remote sensing image is greater than the preset terrain threshold, the corresponding area to be recognized is classified as mountains and rivers. Through the above technical solution, the present invention can recognize remote sensing images and identify terrain conditions (mountains and rivers). BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 It is a structural diagram of the system of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0023] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0024] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0025] The display screen is used to display the user interface of each application.

[0026] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0027] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a big data driven remote sensing image recognition and classification method, including: Step 101, based on the remote sensing image, obtaining a region to be identified on the remote sensing image, and extracting image features and scales of the region to be identified; Step 102, setting a terrain condition recognition model, and calculating a comprehensive measurement value of the terrain condition of the area to be recognized in the remote sensing image according to the image features and scale of the area to be recognized; Specifically, the terrain condition recognition model includes: , in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

[0028] Specifically, they include: .

[0029] Specifically, in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: , in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

[0030] Specifically, The weight of image features include: , in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, is the area adjacent to the area to be identified at position The gray value at .

[0031] Step 103, comparing the comprehensive measurement value of the terrain conditions in the remote sensing image with a preset terrain threshold, if the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, classifying the corresponding area to be identified as mountains.

[0032] Example 2 like Figure 2 As shown, the embodiment of the present invention also provides a big data driven remote sensing image recognition and classification system, including: An information acquisition module is used to acquire the area to be identified on the remote sensing image according to the remote sensing image, and extract the image features and scale of the area to be identified; A model module is set up to set a terrain condition recognition model, and calculate a comprehensive measurement value of the terrain condition of the area to be recognized in the remote sensing image according to the image features and scale of the area to be recognized; Specifically, the terrain condition recognition model includes: , in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

[0033] Specifically, they include: .

[0034] Specifically, in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: , in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

[0035] Specifically, The weight of image features include: , in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, is the area adjacent to the area to be identified at position The gray value at .

[0036] The classification module is used to compare the comprehensive measurement value of the terrain conditions in the remote sensing image with a preset terrain threshold. If the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, the corresponding area to be identified is classified as mountains.

[0037] Example 3 An embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the big data driven remote sensing image recognition and classification method.

[0038] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0039] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, based on a remote sensing image, obtaining a region to be identified on the remote sensing image, and extracting image features and scales of the region to be identified; Step 102, setting a terrain condition recognition model, and calculating a comprehensive measurement value of the terrain condition of the area to be recognized in the remote sensing image according to the image features and scale of the area to be recognized; Specifically, the terrain condition recognition model includes: , in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

[0040] Specifically, they include: .

[0041] Specifically, in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: , in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

[0042] Specifically, The weight of image features include: , in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, is the area adjacent to the area to be identified at position The gray value at .

[0043] Step 103, comparing the comprehensive measurement value of the terrain conditions in the remote sensing image with a preset terrain threshold, if the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, classifying the corresponding area to be identified as mountains.

[0044] Specifically, the clusters are: vegetation cluster, water cluster, building cluster, geological activity cluster and mountain cluster.

[0045] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the big data-driven remote sensing image recognition and classification method.

[0046] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0047] Among them, the storage medium can be used to store software programs and modules, such as a big data driven remote sensing image recognition and classification method in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned big data driven remote sensing image recognition and classification method. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0048] The processor can call the information and application program stored in the storage medium through the transmission system to perform the following steps: Step 101, based on the remote sensing image, obtain the area to be identified on the remote sensing image, and extract the image features and scale of the area to be identified; Step 102, setting a terrain condition recognition model, and calculating a comprehensive measurement value of the terrain condition of the area to be recognized in the remote sensing image according to the image features and scale of the area to be recognized; Specifically, the terrain condition recognition model includes: , in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

[0049] Specifically, they include: .

[0050] Specifically, in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: , in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

[0051] Specifically, The weight of image features include: , in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, is the area adjacent to the area to be identified at position The gray value at .

[0052] Step 103, comparing the comprehensive measurement value of the terrain conditions in the remote sensing image with a preset terrain threshold, if the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, classifying the corresponding area to be identified as mountains.

[0053] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0054] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0055] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0056] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0057] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0058] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0059] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A big data driven remote sensing image recognition and classification method, characterized in that: include: According to the remote sensing image, an area to be identified on the remote sensing image is obtained, and image features and scales of the area to be identified are extracted; Setting a terrain condition recognition model, and calculating a comprehensive measurement value of the terrain condition of the area to be identified in the remote sensing image according to the image features and scale of the area to be identified; The comprehensive measurement value of the terrain conditions in the remote sensing image is compared with a preset terrain threshold. If the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, the corresponding area to be identified is classified as mountains.

2. A big data driven remote sensing image recognition and classification method as claimed in claim 1, characterized in that: The terrain condition recognition model includes: ， in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

3. A big data driven remote sensing image recognition and classification method as claimed in claim 2, characterized in that: include: 。 4. A big data driven remote sensing image recognition and classification method as claimed in claim 3, characterized in that: In time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: ， in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

5. A big data driven remote sensing image recognition and classification method as claimed in claim 2, characterized in that: No. The weight of image features include: ， in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, The adjacent area to the area to be identified is located at The gray value at .

6. A big data driven remote sensing image recognition and classification system, characterized in that: include: An information acquisition module is used to acquire the area to be identified on the remote sensing image according to the remote sensing image, and extract the image features and scale of the area to be identified; A model module is set up to set a terrain condition recognition model, and calculate a comprehensive measurement value of the terrain condition of the area to be recognized in the remote sensing image according to the image features and scale of the area to be recognized; The classification module is used to compare the comprehensive measurement value of the terrain conditions in the remote sensing image with a preset terrain threshold. If the comprehensive measurement value of the terrain conditions in the remote sensing image is greater than the preset terrain threshold, the corresponding area to be identified is classified as mountains.

7. A big data driven remote sensing image recognition and classification system as claimed in claim 6, characterized in that: The terrain condition recognition model includes: ， in, is a comprehensive measure of the terrain conditions in remote sensing images. is the scale of the remote sensing image, is the area to be identified on the remote sensing image, is the area of ​​the region to be identified, is the sub-region of the region to be identified, is the scale of the remote sensing image The number of image features on For the The weight of the image features, For scale Upper position Place image features, For location The horizontal axis of For location The vertical coordinate of For remote sensing images at location The gray value at is the end point of the time integration, is the starting point of the time integration, For in time Remote sensing images at location The gradient vector at For in time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient.

8. A big data driven remote sensing image recognition and classification system as claimed in claim 7, characterized in that: include: 。 9. A big data driven remote sensing image recognition and classification system as claimed in claim 8, characterized in that: In time Remote sensing images at location The gradient vector at The nonlinear diffusion coefficient include: ， in, is the first adjustment factor, is the second adjustment factor, is the third adjustment factor, is the fourth adjustment factor.

10. A big data driven remote sensing image recognition and classification system as claimed in claim 7, characterized in that: No. The weight of image features include: ， in, For the The first adjustment factor, is the third adjustment factor, is the fourth adjustment factor, For the The second adjustment factor, The adjacent area to the area to be identified is located at The gray value at .

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