关键词: 图谱聚类, K均值算法, 极化相似性参数, 相似性, 采样

Abstract: Aiming at the classification of polarimetric Synthetic Aperture Radar (SAR) images, a new approach using spectral clustering was propsed. The propsed method combined the polarimetic similarities of targets and spectral theory. It took two scattering parameters of the similarity characteristics of the target as input and used two-dimensional graph weights function instead of one-dimensional graph weights function to seek power value. It made the sampling points classification and the feature vector classification consistent to realize the classification of polarimetic SAR image. Experimental results show that the classification got by the method above is better than that got by K-means methods.

Key words: spectral classification, K-means algorithm, polarimetric similarity parameter, similarity, sampling