Synthetic Aperture Radar Image Classification Using Deep Learning

Abstract

Powerful remote sensing tools like Synthetic Aperture Radar (SAR) can detect targets and classify land cover, as well as give useful data for monitoring disasters and other uses. Different Deep Learning processes have made a remarkable changes in the past few years, in terms of precisionand effectiveness of SAR picture classification. To improve the precision and dependability of SAR picture interpretation, this research paper presents a thorough investigation of SAR image categorization utilising deep learning techniques, such as convolutional neural networks (CNNs) and recurrent models. We review the state of the art now, suggest a fresh approach, and indicate potential future research possibilities. Our findings show that deep learning is excellent at classifying SAR images, laying the groundwork for more advanced remote sensing applications.