## 1 Introduction  
With the rapid development of sensor technology, it is easier to obtain high-quality, high spatial resolution remote-sensing images [17 ]. Therefore, research into using remote-sensing images to extract strategic targets (such as harbors, airports, ships, etc.) becomes very important [1??? ]. However, existing target recognition algorithms are not suitable for remote-sensing images with complex backgrounds. Therefore, 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 also one of the great focal points for research in the field of pattern recognition and image processing [19? ]. However, 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) [1? ], harbor detection method based on coastline closure (HDC) [1 ], and harbor detection method based on breakwater (HDB) [1? ]. We list the advantages and disadvantages of these methods in Table 27. Harbor detection method based on dock uses the shape of the dock to determine whether the dock exists. First, this method obtains the feature information of the dock, then the dock candidates are extracted; finally, the dock target is identified by matching the feature points. The disadvantage of this method is that the entire harbor region cannot be accurately identified. Recently, [PERSON] and [PERSON] have produced valuable research on this method [27 ]. Harbor detection method based on coastline closure uses the characteristics of the strong closure of the harbor target. This method extracts the entire closed regions as harbor targets by establishing a closed region model; it may mistake some natural terrains for harbor targets. [PERSON] made a detailed study of this method [27 ]. Harbor detection method based on breakwater uses the characteristics of water surrounding both sides of the harbor breakwaters. First, the breakwater candidates are extracted by edge detection, then the long-striped features of the breakwaters are used to remove the pseudo breakwaters. Finally, the breakwaters of the same harbor are merged by the regional combination method. This method has two problems in the process of extracting the harbor targets: One is that false alarms may occur, and the other is that the extracted harbor targets are breakwaters, so the harbor target regions are incomplete. [PERSON] made a detailed study of this method [27 ]. In general, previous research extracted harbor targets by detecting docks, breakwaters and closed regions [27 ]; these methods may lead misidentification and identification of incomplete harbors. A complete harbor target includes its semi-closed geometry, more than two docks and container stacking fields; these features clearly distinguish between harbors and some natural terrains. However, these features are difficult to obtain by traditional image segmentation; moreover, finding them in high-resolution remote-sensing images also requires a lot of calculations. 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. With the development of the SIFT feature detection algorithm [27 ], more and more methods based on SIFT are being used in target extraction [27 ]. Therefore, our algorithm uses SIFT to extract the keypoints of harbor targets. In order to improve the efficiency and accuracy of the harbor target extraction, we have improved the process of SIFT keypoints extraction.