Unsupervised PolSAR image classification based on ensemble partitioning

This work introduces an unsupervised classification framework based on ensemble partitioning for polarimetric synthetic aperture radar (PolSAR) data, which can automatically determine the number of categories. First, the PolSAR image is divided into patches by an over-segmentation method. Second, ensemble partitioning is performed on the patch based dataset to obtain an ensemble similarity matrix. Third, a self-tuning spectral clustering method is adopted to automatically find the number of categories and the classification results, which is finally smoothed by a Markov random field based method. The experimental results on PolSAR image show the effectiveness of this unsupervised classification method.