Target Recognition and Classification of Synthetic Aperture Radar Images

Regardless of the weather or time of the day, Synthetic Aperture Radar is a cornerstone in the field of remote sensing and provides essential information on the characteristics on Earth's surface. One of the main advantages of Synthetic Aperture Radar is its all-weather capability, which renders it highly reliable for imaging in adverse weather conditions including clouds, rain, and darkness. This study provides a brief analysis of object detection in SAR images using the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, with MATLAB serving as the implementation platform. A customized model was developed to accomplish the stated goal, and accuracy levels of 99.64% were recorded for the same. It was found that the deployed model is as efficient as state-of-the-art techniques.