A Hybrid Deep Learning based Automatic Target Detection and Recognition of Military Vehicles in Synthetic Aperture Radar Images - Banu, Abdul Karim Sait Shakin; Shahul Hameed, Kopuli Ashkar; Vasuki, Perumal (International Journal of Industrial Engineering: Theory, Applications and Practice, 2024)
ATR SAR Imagery is the major application in detection and recognition of military vehicles such as armored vehicles, tank, bulldozer, cannon etc. A robust method that employs Markov Random Field and hybrid Googlenet and VGGnet Convolutional Neural Networks (CNNs) have been proposed in this paper. The performance of Synthetic Aperture Radar (SAR) images is degraded by speckle noise and hence in the first module, we performed SAR image despeckling in order to reduce speckle noise in the images using the improved Adaptive Morphological filter. After despeckling, in the second module, the military vehicles or targets are detected from the despeckled images by Markov Random Field segmentation algorithm. Finally in the third module, hybrid Googlenet and VGGnet Convolutional Neural network with SVM classifier is adopted for classifying and recognizing the military targets from the SAR images. The proposed markov random field with the hybrid VGGnet and Googlenet (VGG-GoogleNet) pretrained Convolutional Neural Networks notably improves the recognition accuracy compared with the conventional deep learning CNN based methods.