Polarimetric SAR image classification method based on DCGAN

Abstract

The invention discloses a polarimetric SAR image classification method based on DCGAN. The method comprises the following steps: 1) obtaining an odd-order scattering coefficient, an even-order scattering coefficient, and a volume scattering coefficient, establishing a characteristic matrix F based on pixel points; 2) normalizing each element value in the characteristic matrix F based on pixel points to [0,1], and calling a result of normalization as a feature matrix F1; 3) replacing each element in the feature matrix F1 by 64x64 image blocks around each elements, to obtain a feature matrix F2 based on the image blocks; 4) establishing a feature matrix W1 of a no-label training dataset D1 and a feature matrix W2 of a labeled training dataset D2; 5) establishing a feature matrix W3 of a superpixel clustering center of a testing dataset T; 6) obtaining a trained training network model DCGAN; 7) establishing a determining classification network model, and then through the determining classification network model, classifying the feature matrix W3. The method can realize classification of a polarimetric SAR image, and classification precision is relatively high.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a polarimetric SAR image classification method based on DCGAN. Background technique [0002] Polarization SAR is a high-resolution active microwave remote sensing imaging radar, which has the advantages of all-weather, all-time, high resolution, side-view imaging, etc., and can obtain richer information on targets. The purpose of polarimetric SAR image classification is to use the polarization measurement data obtained by airborne or spaceborne polarimetric SAR sensors to determine the category to which each pixel belongs. It has a wide range of applications in agriculture, forestry, military, geology, hydrology, and ocean research and application value. The classic polarimetric SAR image classification methods are: [0003] In 1992, Lee et al. researched that the multi-view polarization SAR image can be expressed in the form of polarization covariance matrix, and the ma...

AI Technical Summary

Problems solved by technology

The classic polarization SAR image classification method is difficult to adapt to more and more polarization SAR data, so it is difficult to fully learn and utilize the distribution characteristics of polarization SAR data, it is difficult to extract good features, and it cannot achieve high classification accuracy

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