An Energy-Driven Total Variation Model for Segmentation and Classification of High Spatial Resolution Remote-Sensing Imagery

An energy-driven total variation (TV) formulation is proposed for the segmentation of high spatial resolution remote-sensing imagery. The TV model is an effective tool for image processing operations such as restoration, enhancement, reconstruction, and diffusion. Due to the relationship between the TV model and the segmentation problem, in this letter, a TV-based approach is investigated for segmentation of high-spatial-resolution remote-sensing imagery. Subsequently, an object-based classification method, i.e., majority voting, is used to classify the segmented results. In experiments, the proposed TV-based method is compared with the widely used fractal net evolution approach and the clustering segmentation methods such as the expectation–maximization and $k$ -means. The performances of the segmentation and the classification are evaluated based on both thematic and geometric indices.