Classification-oriented hyperspectral and PolSAR images synergic processing

Classification is one of the most important applications in the field of remote sensing. How to improve the accuracy of classification is the critical topic that has long obsessed the researchers. In this paper, a fusion method based on a synergic use of hyperspectral data and Polarimetric SAR (PolSAR) data is presented. This method consists of two main parts, feature-level fusion and decision-level fusion. In feature-level, parallel feature combination strategy is introduced to classification of remote sensing images. Results of feature-level fusion are used as inputs of decision-level fusion based on fuzzy set theory. The final results are compared with processing of single level and single data set, and it shows that the synergic method proposed in this paper has a superior performance in joint classification of hyperspectral and polarimetric SAR data.