Polarimetric SAR Image Classification Based on Discriminative Dictionary Learning Model

Polarimetric SAR (Po1SAR) image classification is one of the important applications of Po1SAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in Po1SAR image classification, however for Po1SAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for Po1SAR classification.