Generative and adversarial network-based polarimetric SAR image classification method

Abstract

The invention discloses a generative and adversarial network-based polarimetric SAR image classification method, and mainly solves the problem of low classification precision caused by unreasonable selection of polarimetric SAR image features in the prior art. Unlabeled and labeled samples are fully utilized. A complex cost function does not need to be designed. The method comprises the followingimplementation steps of (1) inputting a coherence matrix of to-be-classified polarimetric SAR images; (2) performing filtration; (3) generating a sample set; (4) selecting samples; (5) constructing generative and adversarial networks; (6) training the generative and adversarial networks; (7) classifying the sample set; and (8) outputting category labels of all the samples in the sample set of thepolarimetric SAR images. The method has the advantage of remarkable polarimetric SAR image classification effect, and can be further used for target detection and target identification of the polarimetric SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarization synthetic aperture radar SAR (Synthetic Aperture Radar) image classification method based on a generating confrontation network in the technical field of target recognition. The invention can be used for ground object classification and target recognition on polarimetric synthetic aperture radar SAR images. Background technique [0002] The purpose of polarization SAR image classification is to use the polarization measurement data obtained by airborne or spaceborne polarization sensors to determine the category to which each pixel belongs, that is, to give the separation of ground features contained in each pixel, such as oceans, cities, forest etc. The classification of polarimetric SAR data is to use the obtained polarimetric imaging data to classify according to pixels, and the obtained results can represent different ground object information. ...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the feature richness of the polarimetric SAR image information to be classified is high, and the calculation amount is large

The disadvantage of this method is that the features need to be preprocessed before classification, and the classification effect depends on the expression form of the data.

The disadvantage of this method is that this method designs a more complex cost function based on the assumption of the data distribution, the training process is complex and cannot make full use of unlabeled samples.

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