Classification of fully polarimetric SAR images based on ensemble learning and feature integration
Polarimetric Synthetic Aperture Rader (PolSAR) image classification is an important topic of remote sensing image interpretation and application. PolSAR image classification is actually a high dimensional nonlinear mapping problem. Through the use of multiple learning to solve the same problem, ensemble learning can obtain stronger generalization ability than individual classifier. Therefore, in this paper, a PolSAR image classification method based on ensemble learning is proposed, in which the individual pattern classifiers are combined based on Bagging and Boosting ensemble learning to reach an stronger generalization ability and better classification. The verification tests are conducted using EMISAR L-band fully polarimetric data to validate the utility and potential of the proposed method in PolSAR image classification.