Codebook-Based Hierarchical Polarization Feature for Unsupervised Fine Land Classification Using High-Resolution PolSAR Data

摘要

We propose a codebook-based hierarchical polarization feature vector generation to realize an unsupervised land classification with high-resolution PolSAR data. PolSAR has reached the high-resolution of decimeter level. Conventional methods perform the spatial averaging in 10m to 20m square real-space area to classify observed land into categories such as farm, forest, and town. However, with this averaging, we can not expect to discover new detailed land classes by resolution improvement, since the resolution of the PolSAR data is lowered in the averaging process. Our proposal in this paper generates feature vectors useful for classifying the land pieces into categories while preserving the detailed polarization features in respective pixels of the high-resolution PolSAR data. Then, the method discovers the new detailed land classes that can be available only in the high-resolution PolSAR data.