Polsar Data Online Classification Based On Multi-View Learning

Polarimetric synthetic aperture radar (PolSAR) plays an indispensable part in remote sensing. With its development and application, rapid and accurate online classification for PolSAR data becomes more and more important. PolSAR data can be depicted by different features such as polarimetric, texture and color features, which can be considered as multiple views. In this paper, we propose an online multi view learning method based on the passive aggressive algorithm, named OMVPA, for PolSAR data real-time classification. The OMVPA method makes full use of the consistency and complementary properties of different views. Experimental results on real PolSAR data demonstrate that the proposed method maintain a smaller mistake rate compared with other methods.