Characteristic value distribution statistical property-based polarized SAR image classification method

Abstract

The invention discloses a characteristic value Gaussian statistical property-based polarized synthetic aperture radar (SAR) image classification method, which mainly solves the problems that the prior art is insufficient on cognition of characteristic distribution properties and the category judgment limit needs man-made determination. The method comprises the following steps of: 1) performing characteristic value decomposition on all pixel points of polarized SAR images to be classified; 2) selecting different homogeneous regions as the most basic category representative regions, and extracting characteristic values for representing the homogeneous regions; 3) estimating Gaussian hybrid model parameters of the characteristic values lambda 1, lambda 2 and lambda 3 of the homogeneous regions by adopting an expectation-maximization (EM) algorithm respectively, and solving a probability density distribution function of the characteristic values; 4) solving a joint probability distribution function of three characteristic values of each homogeneous region; and 5) performing Bayesian classification on the pixel points in the homogeneous regions, and outputting the classification results. The method has the advantage of remarkable classification effect on the polarized SAR images, and can be used for target detection and target identification of the polarized SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to polarization SAR image classification, and can be used for radar target detection and target recognition. Background technique [0002] Synthetic aperture radar SAR uses the principle of synthetic aperture to improve the azimuth resolution, and uses pulse compression technology to improve the range resolution, so as to obtain better performance than real aperture radar. Polarized SAR belongs to the category of SAR. Compared with traditional SAR, it uses multi-channel transceiver electromagnetic wave technology, and can obtain a more comprehensive understanding of the target through the interpretation of its different channels. The understanding and interpretation of polarimetric SAR images belongs to the category of image processing, and also involves many disciplines such as signal processing, pattern recognition and machine learning. As one of the key links in polarimetric ...

AI Technical Summary

Problems solved by technology

Among them, when using the freeman decomposition method to obtain features to classify polarimetric SAR images, there is a lack of cognition of the feature distribution characteristics; when using the H / alpha classifier for category determination, there is a deficiency that the category decision boundary needs to be determined manually. Insufficient limits its wide application in polarimetric SAR image classification

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