Multi-feature sea–land segmentation based on pixel-wise learning for optical remote-sensing imagery

ABSTRACT

Robust sea–land segmentation in optical remote-sensing images is challenging because of the complex sea–land environment and scene diversity. Here, we propose a novel multi-feature sea–land segmentation method via pixel-wise learning for optical remote-sensing images. Multiple features such as greyscale, local statistical information, edge, texture, and structure are first extracted from each pixel in training images and then used to learn a multi-feature sea–land classifier, which transforms the segmentation issue into pixel-wise binary classification problem. In our approach, a new multi-feature sea–land segmentation algorithm is put forward based on the approximation of Newton method. Experiments on Google-Earth, Venezuelan Remote Sensing Satellite-1 (VRSS-1) and Gaofen-1 images demonstrate that the proposed approach yields more robust and accurate sea–land segmentation results.

Acknowledgments

The work was supported by the National Natural Science Foundation of China under the Grants 61671037 and 61273245, the Beijing Natural Science Foundation under the Grant 4152031, the funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, under the Grant BUAA-VR-16ZZ-03, and the Fundamental Research Funds for the Central Universities under the Grant YWF-16-BJ-J-30.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China under the Grants 61671037 and 61273245, the Beijing Natural Science Foundation under the Grant 4152031, the funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, under the Grant BUAA-VR-16ZZ-03, and the Fundamental Research Funds for the Central Universities under the Grant YWF-16-BJ-J-30.