Complex Matrix And Polarimetric Feature Joint Learning For Polarimetric Sar Image Classification

Abstract

Nearest-regularized subspace (NRS) algorithm is an effective tool to obtain both accuracy and speed for PolSAR image classification. However, existing NRS-based methods only use the polarimetric feature vector as the input, which cannot learn the complex matrix structure and channel information. To learn the complex matrix and scattering features collaboratively, a novel complex matrix and polarimetric feature joint learning method is proposed for PolSAR image classification. Specifically, firstly, a Riemannian NRS model is utilized to learn matrix structure by constructing complex matrix dictionary and Riemannian distance metric. Then, the complex matrix and extracted scattering features are joint learned by the proposed model by constructing coupled dictionaries and different distance metrics. Besides, superpixels are utilized to suppress the speckle noises and reduce the computing time greatly. Experiments are conducted on the real PolSAR data and the results demonstrate the effectiveness of the proposed method.