A number-of-classes-adaptive unsupervised classification framework for SAR images

In this paper, we present a number-of-classes-adaptive unsupervised classification framework for synthetic aperture radar (SAR) images. The framework aims at the provision of robust classification for SAR images even if the number of classes existing in the scene is unknown. It mainly consists of estimation of the number of classes, extraction of each class center, classification of image patches, and integration of spatial relations between patches. The experiment on a TerraSAR-X SAR image shows that the proposed framework presents a promising performance for SAR image classification.