Scene interpretation for SAR images using supervised topic models

In this paper, we present a scene interpretation framework for Synthetic Aperture Radar (SAR) images, using keywords of the image contents provided by users. The framework consists of incorporation of prior knowledge with SAR iMage Annotation Tool (SARMAT), representation of SAR images, and prediction of scene labels based on the supervised Latent Dirichlet Allocation (sLDA) model. The experiment on a TerraSAR-X SAR image shows that the proposed framework provides a promising performance for SAR image scene interpretation.