HIERARCHICAL CLASSIFICATION OF POLARIMETRIC SAR IMAGE BASED ON STATISTICAL REGION MERGING

Abstract. Segmentation and classification of polarimetric SAR (PolSAR) imagery are very important for interpretation of PolSAR data. This paper presents a new object-oriented classification method which is based on Statistical Region Merging (SRM) segmentation algorithm and a two-level hierarchical clustering technique. The proposed method takes full advantage of the polarimetric information contained in the PolSAR data, and takes both effectiveness and efficiency into account according to the characteristic of PolSAR. A modification of over-merging to over-segmentation technique and a post processing of segmentation for SRM is proposed according to the application of classification. And a revised symmetric Wishart distance is derived from the Wishart PDF. Segmentation and classification results of AirSAR L-band PolSAR data over the Flevoland test site is shown to demonstrate the validity of the proposed method.