Target Recognition

Target recognition, the automated identification of objects in imagery, is a crucial area of research with applications spanning military surveillance, autonomous navigation, and remote sensing. Current research focuses on improving the robustness and efficiency of target recognition models, particularly in challenging conditions like noisy synthetic aperture radar (SAR) imagery, often employing deep learning architectures such as convolutional neural networks (CNNs), vision transformers (ViTs), and graph neural networks (GNNs), along with techniques like contrastive learning and attention mechanisms to enhance feature extraction and classification accuracy. Advances in this field are vital for improving the performance of autonomous systems and enabling more effective analysis of complex datasets across various sensor modalities.