Polarized SAR Image Classification Method Based on Residual Learning and Conditional GAN

Abstract

A polarimetric SAR image classification method based on residual learning and conditional GAN, the steps of which are: (1) constructing a conditional GAN ​​generator; (2) constructing a conditional GAN ​​discriminator; (3) to classify the polarimetric SAR image (4) Perform Pauli decomposition on the filter scattering matrix; (5) Normalize the feature matrix; (6) Generate training data set and test data set; (7) Residue the deep and shallow features in the generator Difference learning; (8) Classify the features after residual learning; (9) Obtain the classification accuracy rate; (10) Train the generator of the conditional GAN; (11) Classify the test data set. The invention performs residual learning on the deep and shallow features of the polarimetric SAR image obtained in the generator, and extracts comprehensive feature information, so that the area consistency of the classification result map is good and the classification accuracy is high.

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a polarization synthetic aperture radar (SAR) image classification method based on residual learning and conditional generation confrontation network GAN (Generative Adversarial Networks) in the technical field of radar image classification . The invention can be used to classify ground objects in polarimetric SAR images. Background technique [0002] Polarization synthetic aperture radar is a high-resolution active microwave remote sensing imaging radar with all-weather and all-weather working capabilities, high resolution, and the ability to effectively identify camouflage and penetrate cover. SAR is a full-polarization measurement, which can obtain more information about the target, so it is widely used in remote sensing and map surveying and other fields. [0003] With the further development of full-polarization SAR remote sensing technology and the ...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that the last layer of features of the fully convolutional network is used as the result of polarization SAR ground object classification, and the shallow features are lost, so that there are many messy spots in the map of ground object classification results. Small image spots, poor regional consistency

However, the disadvantage of this method is that the context information of slice features is incomplete, resulting in low classification accuracy.

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