Polarimetric SAR image classification method based on residual learning and conditional GAN

Abstract

The invention discloses a polarimetric SAR image classification method based on residual learning and a conditional GAN, and the method comprises the steps: (1), constructing a generator of the conditional GAN; (2), constructing a discriminator of the conditional GAN; (3), filtering a to-be-classified polarization SAR image; (4), performing pauli decomposition of a filtered scattering matrix; (5),normalizing a feature matrix; (6), generating a training data set and a test data set; (7), performing residual learning of deep and shallow features in the generator; (8), classifying features afterresidual learning; (9), obtaining a classification correctness rate; (10), training the generator of the conditional GAN; (11), classifying test data set. The method achieves the residual learning ofthe deep and shallow features of a polarimetric SAR image in the generator, achieves the extraction of the comprehensive feature information, achieves the good regional consistence of a classification result image, and is high in classification precision.

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 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 tech...

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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