Freeman decomposition and homo-polarization rate-based polarized synthetic aperture radar (SAR) image classification method

Abstract

The invention discloses a Freeman decomposition and homo-polarization rate-based polarized synthetic aperture radar (SAR) image classification method for mainly solving the problems of higher calculation complexity and poor classification effect in the prior art. The method comprises the following steps of: (1) inputting a covariance matrix of polarized SAR data; (2) performing Freeman decomposition on the input matrix to acquire three types of scattering power matrixes of plane scattering, dihedral angle scattering and volume scattering; (3) performing initial division on the polarized SAR data according to the three types of scattering power matrixes; (4) calculating the homo-polarization rate of all pixel points of the polarized SAR data of each class; (5) selecting a threshold value, and dividing the polarized SAR data of each class in the step (3) into 3 classes according to the homo-polarization rate, so that the whole polarized SAR data are divided into 9 classes; and (6) performing repeated Wishart iteration and coloring on the division result of the whole polarized SAR data to obtain a final color classification result graph. Compared with the classical classification method, the method has the advantages that the division of the polarized SAR data is stricter, the classification result is obvious and the calculation complexity is relatively low.

Description

technical field [0001] The invention belongs to the technical field of image data processing, in particular to an image classification method, which can be used to classify polarimetric SAR data. Background technique [0002] With the development of radar technology, polarimetric SAR has become the development trend of SAR. Polarimetric SAR can obtain more abundant target information, and has a wide range of research and applications in agriculture, forestry, military, geology, hydrology and oceans. Value, such as identification of ground object types, crop growth monitoring, yield assessment, land object classification, sea ice monitoring, land subsidence monitoring, target detection and marine pollution detection, etc. The purpose of polarimetric image classification is to determine the class to which each pixel belongs, using polarimetric data obtained from airborne or spaceborne polarimetric sensors. Classical polarization SAR classification methods include: [0003] C...

AI Technical Summary

Problems solved by technology

One of the shortcomings of H / α classification is that the division of regions is too arbitrary. When the data of the same class is distributed on the boundaries of two or more classes, the performance of the classifier will deteriorate. Another shortcoming is that when the data coexist in the same area When there are several different ground features, it will not be able to effectively distinguish

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

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