SAR Object Detection Using a Novel Convolutional Neural Network Architecture

Abstract

In recent years, the importance of SAR (synthetic aperture radar) image analysis is growing day by day due to the vast applications in the field of oceanography, military war, land observation, agriculture, disaster management and geography. These applications required accurate classification of highresolution SAR images. In the present study, two different machine learning techniques are applied on the SAR dataset to analyze the classification accuracy. The first classification technique is SVM (support vector machine) along with principal component analysis (PCA) for feature reduction. While, in the second technique, a novel CNN (convolutional neural network) has been proposed to perform the classification on SAR images. Essential experimental analysis has been carried out based on the MSTAR dataset. Experimental results of the current study showed that the proposed CNN model outperformed the SVM classifier with an accuracy of 98.69%