Forest Mapping and Classification with Compact PolInSAR Data
Forest Mapping and Classification with Compact PolInSAR Data

An unsupervised classification method was applied to compact polarimetric-interferometric SAR(C-PolInSAR) data to investigate its potential for forest mapping and classification. Unsupervised classification requires an initial class as a training set. In this paper, the compact polarimetric entropy H and the optimal coherence spectrum A were computed, and their capabilities for initial classification were analyzed. Based on the H and A, a partition method was proposed to subdivide the H-A plane, and initial classes were hence obtained. Next, unsupervised C-PolInSAR segmentation procedures based on H-A and the complex coherence matrix J4 were investigated. The effectiveness of the unsupervised classification of C-PolInSAR data was demonstrated by using an E-SAR L-band PolInSAR dataset of the Traunstein test site.
An unsupervised classification method was applied to compact polarimetric-interferometric SAR(C-PolInSAR) data to investigate its potential for forest mapping and classification. Unsupervised classification requires an initial class as a training set. In this paper, the compact polarimetric entropy H and the optimal coherence spectrum A were computed, and their capabilities for initial classification were analyzed. Based on the H and A, a partition method was proposed to subdivide the H-A plane, and initial classes were hence obtained. Next, unsupervised C-PolInSAR segmentation procedures based on H-A and the complex coherence matrix J4 were investigated. The effectiveness of the unsupervised classification of C-PolInSAR data was demonstrated by using an E-SAR L-band PolInSAR dataset of the Traunstein test site.