Deep Learning Solutions for Analysis of Synthetic Aperture Radar Imageries

Abstract

The potential of Synthetic Aperture Radar (SAR) to detect surface and subsurface characteristics of land, sea, and ice using polarimetric information has long piqued the interest of scientists and researchers. Traditional strategies include employing polarimetric information to simplify and classify SAR images for various earth observation applications. Deep learning (DL) uses advanced machine learning algorithms to increase information extraction from SAR datasets about the land surface, as well as segment and classify the dataset for applications. The chapter highlights several problems, as well as what and how DL can be utilized to solve them. Currently, improvements in SAR data analysis have focused on the use of DL in a range of current research areas, such as data fusion, transfer learning, picture classification, automatic target recognition, data augmentation, speckle reduction, change detection, and feature extraction. The study presents a small case study on CNN for land use land cover classification using SAR data.