Automatic SAR Change Detection Based on Visual Saliency and Multi-Hierarchical Fuzzy Clustering

Change detection based on multi-temporal synthetic aperture radar (SAR) images plays a significant role in environmental and earth observations. With the advancement in deep neural networks, existing work in the literature mainly concentrates on developing self-supervised methods to generate pseudo-labeled samples to guide the subsequent deep learning based detection. However, this way of selecting sample inevitably introduces erroneous labels and imbalance between unchanged and changed classes, thus causing deterioration in change detection performance. To mitigate these issues, we have proposed a SAR change detection network based on visual saliency and multi-hierarchical fuzzy clustering. Specifically, with multi-dimensional difference feature representations, a visual saliency based difference map is constructed for accurate difference feature extraction. By integrating neighborhood information and hierarchical clustering, the multi-hierarchical fuzzy local information C-means clustering algorithm has been developed to identify potential changed regions for sample selection. A class-balanced adaptive focal loss has further been incorporated into the network training to obtain accurate predictions. Extensive experiments and comparisons on five datasets have been performed. The proposed method has achieved averaged accuracy of 99.07% and Kappa coefficient of 79.87%, outperforming other state-of-the-art algorithms both visually and quantitatively.