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.
Highlights
With the rapid development of sensor technology, it is easier to obtain high-quality, high spatial resolution remote-sensing images [? ]
There are few studies on harbor target extraction; previous studies on harbor target extraction can be grouped into the following categories: Harbor detection method based on dock (HDD) [? ], harbor detection method based on coastline closure (HDC) [? ], and harbor detection method based on breakwater (HDB) [?
In order to solve the problems of harbor target extraction, we propose a new harbor extraction algorithm based on edge preservation and a scale-invariant feature transform keypoints extraction method based on edge categories (EC-SIFT) for high spatial resolution remote-sensing images
Summary
With the rapid development of sensor technology, it is easier to obtain high-quality, high spatial resolution remote-sensing images [? ]. ]. research into using remote-sensing images to extract strategic targets (such as harbors, airports, ships, etc.) becomes very important [? ]. existing target recognition algorithms are not suitable for remote-sensing images with complex backgrounds. In order to extract regions containing meaningful targets for further processing, this article proposes a new target extraction method based on high spatial resolution remote-sensing images. A harbor, as an important military and civil constructions, has important practical value for the fields of ship navigation and military reconnaissance, and they are one of the great focal points for research in the field of pattern recognition and image processing [? There are few studies on harbor target extraction; previous studies on harbor target extraction can be grouped into the following categories: Harbor detection method based on dock (HDD) [? There are few studies on harbor target extraction; previous studies on harbor target extraction can be grouped into the following categories: Harbor detection method based on dock (HDD) [? ], harbor detection method based on coastline closure (HDC) [? ], and harbor detection method based on breakwater (HDB) [?
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