SAR image object classification method based on countermeasure network generated by distribution and structure matching

Abstract

The invention discloses an SAR image object classification method based on distribution and structure matching GAN. The method includes selecting real data in a training set and pseudo data generatedby the generator to train a discriminator in DSM-ACGAN, updating its parameters; fixing discriminator parameters, generatinga generator in a pseudo-data training DSM-ACGAN again and updating its parameters; calculating the difference in distribution and structural characteristics between generated data and real data, and guiding the feature learning in DSM-ACGAN training and discriminator as sample weights; the trained discriminator is used to predict the test SAR image and calculate the classification index. At the same time, the invention integrates the statistics of the real SAR image and the image characteristics into the generated antagonism network as a discriminant a priori, effectively realizes the discriminant feature learning, and remarkably improves the classification performance.

Description

technical field [0001] The invention belongs to the technical field of SAR image processing, and in particular relates to a SAR image object classification method based on distribution and structure matching generation confrontation network. Background technique [0002] SAR is a high-resolution active microwave remote sensing imaging radar. Its observation of the earth is not affected by factors such as illumination and weather, so it is widely used in military and civilian applications. High-resolution SAR image classification is an important part of SAR understanding and interpretation, and plays a vital role in environmental protection, disaster monitoring, ocean observation, resource protection, land cover, precision agriculture, urban detection, and geographic mapping. [0003] Feature extraction is an important part of classification, and the discriminativeness of extracted features largely determines the performance of classification. The existing feature extraction...

AI Technical Summary

Problems solved by technology

However, the former requires a lot of time-consuming expert knowledge to design and complex hyperparameter decisions, while the latter can only get confusing features due to the lack of prior guidance, which inevitably reduces the classification performance.

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