Segmentation and Classification of PolSAR data using Spectral Graph Partitioning

摘要

Polar metric synthetic aperture radar (PolSAR) image classification is an important technique in the remote sensing area, has been deeply studied for a couple of decades. This paper proposes a new approach for segmentation and classification of PolSAR datain two steps. First, segmentation is performed based on spectral graph partitioning using edge information. Graph partitioning process is completed using the normalized cut criterion. Then, classification is performed based on the object level. We use Cloude and Portier's method to initially classify the PolSAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The advantages of this method are the automated classification, and the interpretation of each class based on the region's scattering mechanism. We tested this object-based analysis on our study area. It showed that this result well overcome the pepper-sault phenomenon appearing in the one using traditional pixel-based method, providing robust performance and the results more understandable and easier for further analyses.