Hierarchical segmentation of polarimetric SAR image via Non-Parametric Graph Entropy

PolSAR image segmentation has long been an important problem in the PolSAR remote sensing community. Many segmentation algorithms describe images in terms of a hierarchy of regions has attracted particular attention in recent years. However, they often contain more data than is required for an efficient description. In this paper, we propose an effective measure to extract hierarchical semantic structures from PolSAR images. First, we construct the Binary partition tree (BPT) which is a multi-scale image representation to obtain a hierarchy of regions. Once the tree has been constructed, every hierarchy can be considered as a region adjacency graph (RAG). Second, we use a Non-Parametric Graph Entropy as a measure of graph complexity to identify semantic structures within BPT hierarchies. Experimental results on NASA/JPL AIRSAR and DLR E-SAR images demonstrate the effectiveness of the proposed approach.