SAR Image Classification: A Comprehensive Study and Analysis

Alicia Passah,Samarendra Nath Sur,Babusena Paul,Debdatta Kandar

doi:10.1109/access.2022.3151089

Abstract

Deep learning has obtained wide attention in various fields enabling systems to derive essential information from digital inputs. Lately, the use of deep learning in remote sensing applications has also been motivated and applied, wherein considerable improvements in the results are witnessed. Synthetic aperture radar images have been used in various earth observation systems because of their all-day imaging capacity and self-illuminating nature. Various works concentrating on extracting meaningful information from SAR data for various other applications have been proposed in the literature. Classification of SAR images has been one of the utmost steps in numerous SAR applications. Therefore, this work focuses on studying several existing techniques that use deep learning for SAR image classification by examining the architectures involved. Based on the study, crucial observations are made, highlighting the merits and demerits of several approaches, allowing researchers to better understand how the methods can impact the performance of the deep learning models for SAR image classification in the future. Potential hybrid models for the classification of SAR images are also presented in this paper.

Highlights

W ITH enormous volumes of data produced from Synthetic Aperture Radar (SAR) and many other SAR carrying satellites, the processing and interpretation of SAR data have become immediate for a wide range of applications
This paper considers the performances of various SAR image classification approaches, and a comparison of the different methods has been made, which will help in understanding and solving existing issues bagged with SAR image classification
RESEARCH ISSUES AND CHALLENGES One of the major issues associated with the classification of SAR images, be it a scene or target classification, is the misclassification issue, which means a part or entire image is being predicted incorrectly by the model or the algorithm

Summary

Introduction

W ITH enormous volumes of data produced from Synthetic Aperture Radar (SAR) and many other SAR carrying satellites, the processing and interpretation of SAR data have become immediate for a wide range of applications. With more SAR sensors mounted satellites emerging in the coming years, gigantic data needs to be processed, archived and analyzed. Accessibility of such SAR data is challenging as they are difficult to interpret. A. SAR IMAGE PROCESSING SAR is a radar that is used for constructing two-dimensional images by transmitting electromagnetic waves to the surface of the earth with the help of a transmitter. SAR IMAGE PROCESSING SAR is a radar that is used for constructing two-dimensional images by transmitting electromagnetic waves to the surface of the earth with the help of a transmitter These waves get reflected from the earth’s surface in the form of echos to the receiver of the radar, and images are constructed with respect to the received signals [13].

Methods

Results

Conclusion