Review on polarimetric SAR terrain classification methods using deep learning

Polarimetric synthetic aperture radar (PolSAR) is one of the main sources of remote sensing data, because it can realize all-day and all-weather imaging. Terrain classification is an important research in the field of PolSAR data interpretation, which has become one of the hotspots in the research field and has been widely used in both military and civilian applications. In recent years, deep learning has achieved remarkable results in many research fields, some of which have been made in the field of PolSAR image processing. Compared with traditional image classification methods, the deep learning method has the advantages of automatic extracting deep features, strong generalization and high accuracy. In this paper, the existing terrain classification methods for the PolSAR image based on deep learning are reviewed. According to the different network models in deep learning, the research on PolSAR terrain classification is described in detail from three aspects, that is, deep belief network, sparse autoencoder network and convolutional neural network. Finally, the advantages and disadvantages of PolSAR terrain classification based deep learning are summarized in comparison with classical classification methods. Meanwhile, the development trend of PolSAR terrain classification is analyzed and discussed. © 2023 Science Press. All rights reserved.