A Survey on the Applications of Convolutional Neural Networks for Synthetic Aperture Radar: Recent Advances

In recent years, convolutional neural networks (CNNs) have drawn considerable attention for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR data analysis that have been tackled by CNNs are systematically reviewed, such as automatic target recognition, land use and land cover classification, segmentation, change detection, object detection, and image denoising. Special emphasis has been given to practical techniques such as data augmentation and transfer learning. Complex-valued CNNs, which have been introduced to exploit phase information embedded in SAR complex images, have also been extensively reviewed. To conclude this review paper, open challenges and future research directions are highlighted.

Automated summary

This study systematically reviews the application of convolutional neural networks (CNNs) in the analysis of synthetic aperture radar (SAR) data. Various subareas of SAR data analysis, including automatic target recognition, land use and land cover classification, segmentation, change detection, object detection, and image denoising, are discussed. Practical techniques such as data augmentation and transfer learning are emphasized, along with the introduction of complex-valued CNNs that exploit phase information in SAR complex images. The review paper concludes by highlighting open challenges and future research directions.