Information extraction from very high resolution satellite SAR data time series using graph based connected features

This paper presents information extraction from very high resolution SAR data. Small patches of SAR data were used for graph based regularized sparse coding. The auto regressive model was used to extract sparse graphs from the data. The sparse graph features were used for dictionary learning using Graph-regularized Non-negative Matric Factorization. The algorithm was used categorization of database consisted of 20 classes and each class contained 100 patches. The algorithm was not superior to the supervised categorization using spectral features.