Unsupervised Classification of Spectropolarimetric Data by Region-Based Evidence Fusion

Abstract

Imaging spectropolarimetry is a new sensing method that can acquire the spectral, polarimetric, and spatial information of an interesting scene. They give the incomplete representations of a scene, respectively, and it is expected that combination of them will improve confidence in target identification and quality of the scene description. In this letter, a divide-and-conquer-based unsupervised spectropolarimetric data classification method is proposed to utilize the spatial, spectral, and polarimetric information jointly. First, a spectropolarimetric projection scheme is proposed to divide the whole data set into two parts: spatial-spectral and spatial-polarimetric domains. Then, a nonparametric technique is used to extract the homogeneous regions in these two domains. Each homogeneous region offers a reference spectrum and polarization, based on which a pseudosupervised spectropolarimetric classification scheme is developed by using evidence theory to fuse the information provided by the spectrum and polarization. The experimental results on real spectropolarimetric data demonstrate that the proposed divide-and-conquer-based classification scheme can achieve higher accuracy than the fuzzy c-means clustering method with spatial information constraints, which takes into account the spatial information during spectral and polarimetric clustering. Moreover, the experimental results also show the potential of spectropolarimetric classification.