Texture feature analysis and feature quantity calculation
The system structure adopted in this paper is shown in Figure 1. The images to be classified are preprocessed, so the image preprocessing process is not involved.
The texture feature describes the local patterns and arrangement rules that repeatedly appear in the image, and reflects the law of grayscale change in the macro sense. The texture feature of the local area of ​​the image is one of the main basis for identifying objects.
The gray level co-occurrence matrix represents the frequency distribution xΔyΔ of the pixel pairs whose positions are separated by (,) gray levels h and k, respectively [6]. Assuming that the gray level of the specified area of ​​the image is divided into N levels, the gray level co-occurrence matrix M (xΔ, yΔ) of the area is an N × N matrix, and its elements M (h, k) are apart (xΔ, yΔ) and The gray level is the number of occurrences of pixel pairs in h and k files, respectively, divided by the sum of the logarithms of all (xΔ, yΔ) points in M, where h, k = 1, 2,…, N
The gray level co-occurrence matrix of the SAR image reflects the comprehensive information of the image gray direction, adjacent interval, and change range. It is the basis for analyzing the local pattern of the image and its arrangement rules. Based on this, a series of features describing the image texture can be further extracted. Commonly used statistics are as follows [7]:
1) The second moment or energy of the angle is
The variance variance is similar to the variance variance, except that the sum of the absolute value of the difference between the horizontal and vertical coordinate values ​​in the co-occurrence matrix is ​​n as the density.
9) The difference entropy is 
Research on Target Classification of Synthetic Aperture Radar Images
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