Polarized synthetic aperture radar (SAR) image classification method based on Freeman decomposition and data distribution characteristics

Abstract

The invention discloses a polarized synthetic aperture radar (SAR) image classification method based on Freeman decomposition and data distribution characteristics and mainly solves the problems of high computation complexity and poor classification effect in the prior art. The polarized SAR image classification method includes step: (1) performing Freeman decomposition for polarized SAR images to be classified to obtain plane scattering power, dihedral angle scattering power and volume scattering power; (2) initially dividing the polarized SAR images into three classes according to the three scattering powers; (3) calculating the distribution characteristic parameter xL of each pixel point in each class; (4) subdividing each of the three initially divided classes into three classes according to the distribution characteristic parameters xL to divide the whole polarized SAR images into nine classes; and (5) performing complex Wishart iteration for the obtained nine-class dividing results to obtain the final classification result. Compared with the typical classification method, the polarized SAR image classification method is rigorous in polarized SAR image dividing, good in classification effect and small in computation complexity and can be applied to terrain classification and object identification of the polarized SAR images.

Description

Technical field [0001] The present invention belongs to the field of image processing technology, and relates to the application of polarization synthetic aperture radar SAR image classification field. Specifically, it is a polarization SAR image classification method based on Freeman decomposition and data distribution characteristics, which can be used for polarization SAR Image classification of features. Background technique [0002] With the development of radar technology, polarized SAR has become the development trend of SAR. Polarized SAR can obtain richer target information, and has extensive research and applications in agriculture, forestry, military, geology, hydrology, and oceans. Value, such as the identification of ground feature types, crop growth monitoring, yield evaluation, ground feature classification, sea ice monitoring, land subsidence monitoring, target detection and marine pollution detection, etc. The purpose of polarization image classification is to u...

AI Technical Summary

Problems solved by technology

There are two defects in the H / α classification: one is that the division of regions is too arbitrary; the other is that when several different features coexist in the same region, they cannot be effectively distinguished

This method combines Freeman decomposition and complex Wishart distribution, and has the characteristics of maintaining the purity of the main scattering mechanism of polarimetric SAR. However, due to the multi-class division and merging in Freeman decomposition in this method, the computational complexity is high, and the classification time is relatively high. long

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