Polarization SAR (synthetic aperture radar) image classification method based on Freeman decomposition and PSO (particle swarm optimization)

Abstract

The invention discloses a polarization SAR (synthetic aperture radar) image classification method based on Freeman decomposition and PSO (particle swarm optimization), and mainly aims to solve the problems of higher computational complexity and poor classification effect in the prior art. The implementation steps are as follows: (1) inputting a covariance matrix of polarization SAR data; (2) performing Freeman decomposition on the input matrix to obtain three scattering power matrixes of plane scattering, dihedral angle scattering and volume scattering; (3) initially dividing the SAR data according to the three scattering power matrixes; (4) obtaining two threshold values of each category by virtue of the two-dimensional double threshold value Otsu method on the basis of QPSO (quantum-behaved particle swarm optimization); (5) dividing each initially divided category of the polarization SAR data into three categories, thereby dividing the whole polarization SAR data into 9 categories; (6) carrying out Wishart iteration and coloring on the division result of the whole SAR data to obtain a final color classification result image. Compared with the classical classification method, the polarization SAR image classification method based on Freeman decomposition and PSO is more rigorous in dividing the polarization SAR data, the classification result is obvious, and the computational complexity is relatively small.

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 the polarimetric measurement data obtained by an airborne or spaceborne polarimetric sensor. Classical polarization SAR classification methods inclu...

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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