Enhanced Edge Information and Prototype Constrained Clustering for SAR Change Detection

The utilization of synthetic aperture radar (SAR) imagery for change detection can effectively circumvents the stringent limitations imposed by weather and lighting conditions, and is finding widespread applications in fields such as disaster monitoring and urban research. To address issues of edge blurring, severe noise interference and sample imbalance, an automated SAR change detection framework is proposed based on enhanced edge information and prototype constrained clustering. First, a gradient-based neighborhood ratio (GNR) is designed to reinforce the edge information of the difference map, facilitating robust differential information representations. Subsequently, to obtain accurate samples in an unsupervised manner, we have developed prototype constrained hierarchical clustering for preclassification. The quantity and quality of selected samples can be precisely guaranteed through the utilization of histogram analysis and prototype constraints. In the sample learning and prediction phases, a class-balanced noise-tolerant change detection network is proposed that combines focal loss and mean absolute error loss (MAEL), further tackling the sample imbalance issue, strengthening noise resistance and improving change detection accuracy. Comprehensive experimental results and analysis conducted on five benchmark datasets have validated the effectiveness and robustness of the proposed method.