LAUSR

Sea–land segmentation is a key step for some important applications of panchromatic remote sensing image processing. However, robust and effective sea–land segmentation for high-resolution panchromatic remote sensing images is still a challenging problem. This letter presents an accurate and robust approach by integrating the improved multiscale normalized cut (IMNcut) method and improved Chan–Vese model for sea–land segmentation. At first, the image is downsampled and segmented into multiple regions by the IMNcut method. Next, the homogeneous regions are merged to obtain a coarse segmentation result. Finally, gray intensity and local entropy features are integrated as discriminants of the improved Chan–Vese model, which is used to obtain the final segmentation result through a low- to high-resolution segmentation scheme. Experimental results performed on several real data sets demonstrate the effectiveness of the proposed model in terms of visual and objective evaluations.