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.
Original language | English |
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Article number | 8107673 |
Pages (from-to) | 2443-2447 |
Number of pages | 5 |
Journal | IEEE Geoscience and Remote Sensing Letters |
Volume | 14 |
Issue number | 12 |
DOIs | |
Publication status | Published - Dec 2017 |
Keywords
- Chan-Vese model
- multiscale normalized cut (MNcut)
- panchromatic remote sensing image
- sea-land segmentation