SAR image Interpretation Using CNN and Contrastive Learning

Deep learning-based SAR image interpretation have gained much attention in recent years, in this paper supervised learning for SAR images is discussed first and then novel self supervised contrastive learning method is presented to reduce dependency on large amount of labeled data. SAR images provide valuable information about earth surface. Unlike optical images, SAR images are formed by backscattered signals influenced by surface roughness and other physical properties. Deep learning models can extract details from SAR data that may be missed by traditional image processing. Furthermore, deep learning models can learn patterens and features from SAR images to enable automated interpretation and analysis. It is difficult and time consuming to get labeled data. Supervised CNN is used to extract meaningful data from the SAR images for classification but it uses labeled data extensively, using self-supervised contrastive learning, positive pairs are constructed for each image; positive pairs are generated using augmentation of the SAR image. The contrastive loss function is designed such that it encourages the network to learn similar representation for positive pairs. A novel features extractor is introduced to get robust features. Contrastive loss and backpropagation are used to train the network without labels, after training, fine tuning is done on small number of labels called few shot. Downstream tasks such as classification is performed after fine tuning. Results are evaluated on the widely used SAR bench mark dataset.