Harbor Extraction Based on Edge-Preserve and Edge Categories in High Spatial Resolution Remote-Sensing Images

Abstract

Efficient harbor extraction is essential due to the strategic importance of this target in economic and military construction. However, there are few studies on harbor extraction. In this article, a new harbor extraction algorithm based on edge preservation and edge categories (EC) is proposed for high spatial resolution remote-sensing images. In the preprocessing stage, we propose a local edge preservation algorithm (LEPA) to remove redundant details and reduce useless edges. After acquiring the local edge-preserve images, in order to reduce the redundant matched keypoints and improve the accuracy of the target candidate extraction method, we propose a scale-invariant feature transform (SIFT) keypoints extraction method based on edge categories (EC-SIFT): this method greatly reduces the redundancy of SIFT keypoint and improves the computational complexity of the target extraction system. Finally, the harbor extraction algorithm uses the Support Vector Machine (SVM) classifier to identify the harbor target. The experimental results show that the proposed algorithm effectively removes redundant details and improves the accuracy and efficiency of harbor target extraction.