Method for classifying polarimetric SAR (synthetic aperture radar) images on the basis of Freeman decomposition and spectral clustering

Abstract

The invention discloses a method for classifying polarimetric SAR (synthetic aperture radar) images on the basis of Freeman decomposition and spectral clustering and mainly solves the problem that the existing unsupervised polarimetric SAR classification method is low in classification precision. The method includes the steps of subjecting each pixel to Freeman decomposition, and extracting volume scattering power, dihedral angle scattering power and surface scattering power of each pixel; applying the scattering powers and a coordinate of the pixel as input feature vectors of Mean Shift algorithm, and segmenting images by the Mean Shift algorithm to obtain M regions; selecting representative points of the M regions as input points for spectral clustering, subjecting each region to spectral clustering to obtain results of image pre-classification; and finally subjecting a whole image obtained by pre-classification to iterative classification through a Wishart classifier so as to obtain final classification results. Experimental results show that classification of the polarimetric SAR images by the method is better, and the method is applicable to unsupervised classification of various polarimetric SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to applications in the field of polarization synthetic aperture radar (SAR) image ground object classification, specifically a polarization SAR image classification method based on Freeman decomposition and spectral clustering algorithm, which can be used for polarization Object classification and target recognition in SAR images. 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 pola...

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Problems solved by technology

However, when the algorithm is applied to the field of image segmentation, the amount of calculation and storage is unbearable, which seriously hinders the performance of the algorithm.

And when the Gaussian function is used to construct the similarity matrix, the scale parameter has a great influence on the classification structure and it is difficult to obtain the optimal parameter, which reduces the stability of image segmentation

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