Semisupervised Classification of PolSAR Image Incorporating Labels’ Semantic Priors

Deep learning techniques represented by deep convolutional neural networks (CNNs) have been widely used in polarimetric synthetic-aperture radar (PolSAR) image classification in recent years. One challenge is how to get a pleasant classification result with limited human-labeled samples. In this letter, a novel semisupervised classification method incorporating labels' semantic priors is proposed for PolSAR image classification with limited labeled samples. The core idea is that a good classification result map should have consistent regions and aligned boundaries. Thus, a cost function is proposed, which mainly contains three terms including a supervised term, a region consistency term, and a boundary kept term. The supervised term enforces the category label to be the same with the human-labeled labels. The region consistency term encourages the labels in one region to be consistent. The boundary-kept term constrains the region consistency term, preventing the classification map from being too smooth. An alternate iterative optimization method is proposed to solve this equation. First, a CNN is trained using the labeled samples and the classification probability map. Then, the classification probability map is updated by the prediction of the trained CNN and the labeled samples. Repeat these two procedures until the maximum number of iterations is met. Experiments on two real PolSAR images are conducted to validate the effectiveness of the proposed method compared with several state-of-the-art methods.