Unsupervised spectropolarimetric imagery clustering fusion

In the past few years, imaging spectroscopy has been used widely. However, it only acquires intensity information in a narrow electromagnetic band, ignoring the polarimetric information of the electromagnetic wave, resulting in inaccurate material classification. Imaging spectropolarimetric technology as a new sensing method can acquire the polarimetric information at a narrow electromagnetic band sequence, but there are few results showing how to combine the redundant and complementary features provided by spectropolarimetric imagery. In this paper, an unsupervised spectropolarimetric imagery classification method is proposed to jointly utilize the spatial, spectral and polarimetric information to make material classification more accurate. First, a spectropolarimetric projection scheme is proposed to divide the spectropolarimetric data set into two parts: a polarimetric spectrum data set and a polarimetric data cube. Then, a kernel fuzzy c-means clustering method is used to cluster the polarimetric spectrum data set and polarimetric data cubes. At last, kernel fuzzy c-means clustering results are combined by evidence reasoning to get better clustering performance. Through experimentation and simulation, the effects of classifying different materials with similar surface colour can be enhanced greatly.