Superpixel-based classification using semantic information for polarimetric SAR imagery

Polarimetric SAR classification is an effective approach in image understanding. This paper proposes a novel semantic method for classification of Polarimetric SAR data. The method combines superpixels and semantic model to benefit from both the object-oriented classification and the high-level semantic information. Firstly, pixels was grouped into superpixels via Simple Linear Iterative Clustering (SLIC). Secondly, the feature vector was generated within the superpixels by considering both polarimetric information and textures. To incorporate semantic information, the feature vectors were further processed via probabilistic Latent Semantic Analysis (pLSA). Finally, Supporting Vector Machine (SVM) was utilized to obtain classification results. The results were evaluated with respect to the accuracy of classification and spatial preservation. The results of this work were analyzed by means of RADARSAT-2 data.