Remote sensing image classification method based on gray level co-occurrence matrix and BP neural network
A technology of BP neural network and gray level co-occurrence matrix, which is applied in the field of remote sensing images, can solve problems such as difficulty in data acquisition, difficulty in data acquisition, complex features of remote sensing image spectrum and spatial information, and achieve the effect of improving classification accuracy
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no. 1 example
[0072] like figure 1 As shown, a kind of remote sensing image classification method based on gray level co-occurrence matrix and BP neural network provided by the embodiment of the present invention at least includes the following steps:
[0073] S101. Obtain remote sensing image data in the sampling point area, and perform batch screenshots on the remote sensing image data to obtain a screenshot dataset;
[0074] It should be noted that, in this application, Google Earth (Google Earth) software is used to obtain remote sensing image data of the sampling point area. Google Earth is a 3D image and vector map service software launched by Google in the United States. It allows users to query specific areas in an interactive way, and to control the zoom in, zoom out, and roaming of the map. Its data sources include satellite remote sensing images ( such as Quickbird and Landsat satellites) and aerial imagery. The effective resolution of Google Earth's global images is usually 30...
no. 2 example
[0135] like figure 2 As shown, a remote sensing image classification system 200 based on a gray-level co-occurrence matrix and a BP neural network provided by an embodiment of the present invention includes: a data acquisition module 201, a feature extraction module 202, and a classification processing module 203; wherein,
[0136] The data acquisition module 201 is configured to acquire remote sensing image data in sampling point areas, and perform batch screenshots on the remote sensing image data to obtain a screenshot data set;
[0137] The feature extraction unit 202 is configured to extract the texture features of each picture in the screenshot data set according to the gray level co-occurrence matrix, and use statistical methods to calculate the gray level statistical features of each picture in the screenshot data set;
[0138] The classification processing module 203 is configured to pass the texture feature and the gray statistical feature through a preset BP neural...
no. 3 example
[0140] An embodiment of the present invention also provides a computer terminal device, including: one or more processors;
[0141] a memory, coupled to the processor, for storing one or more programs;
[0142] When the one or more programs are executed by the one or more processors, so that the one or more processors implement the remote sensing image classification based on gray-level co-occurrence matrix and BP neural network as described in any one of the above method.
[0143] It should be noted that the processor can be a central processing unit (Central Processing Unit, CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable Gate array (Field-Programmable GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., the genera...
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