Sea-Land Segmentation for Harbour Images with Superpixel CRF

Sea land segmentation is an important technique in many remote sensing applications, such as coastline surveillance and near-shore ship detection. High resolution satellite optical imaging is able to capture the details in the coastal regions, introducing intra-class variance and interferences like waves, shadow and forestry regions. To overcome such variance and interferences, the high resolution optical satellite image is first over-segmented into super-pixels, i.e., homogeneous regions. Then the conditional random fields (CRFs) are adopted to model the relations of the super-pixels. The optimal labels of all the superpixels are determined by performing the loopy belief propagation on the CRFs. The final segmentation result is obtained by refining the pixelwise probability conditioned on the estimated superpixel labels with edge preserving filtering. Experimental results on Google Earth data of different coastal cities show the effectiveness of the proposed sea land segmentation method.