Recent Advances in SAR Image Analysis Using Deep Learning Approaches: Examples of Speckle Denoising and Change Detection

Abstract

Deep learning is a machine learning technique that has significantly improved results in many areas such as computer vision, speech recognition, machine translation, and biomedical imaging analysis and understanding. Recently in the field of synthetic aperture radar (SAR) images analysis, deep learning approaches have become a powerful tool making information extraction from SAR images very accurate and giving good interpretations related to environment and earth surface observation. This work examines the recent development of scientific productions on the applications of deep learning approaches in the SAR imaging field. These applications concern the speckle noise reduction from SAR images and the algorithms-based method for change detection and classification of these remote sensing images. Moreover, an analysis of scientific production in this field is discussed by exploiting the Scopus database.