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(Journal Article) |
IEEE Geoscience and Remote Sensing Letters 14 (12): 2443-2447 (2017)
Sea—Land Segmentation for Panchromatic Remote Sensing Imagery via Integrating Improved MNcut and Chan—Vese Model
W Liu
,
L Ma
,
H Chen
,
Z Han
,
N Q Soomro
ABSTRACT
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.