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.

An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

“…There will be many false alarms generated by the natural raised terrain using the geometric method. Because the geometric feature of ports is salient, almost all existing port and small harbor detection methods are based on the extraction of the specific geometric features [1][2][3][4][5][6][7][8][9]. Usually, the geometric-based methods mainly include three steps: sea-land segmentation, geometric feature extraction, and feature measure and classification.…”

“…Starting from the assumption that the overall structural characteristics of the port contour are in order, Li et al [4] detected the port by extracting corners on the contour using wavelet transform and described the corners using chain code descriptors. He et al [5] detected a harbor by a combination of edge detection and scale-invariant feature transform keypoint extraction. According to the assumption that the contours of jetties were concave and convex, Chen et al [6] first proposed a method based on jetty scanning and merging.…”

A Multidirectional One-Dimensional Scanning Method for Harbor Detection From SAR Images

“…At present, related researches of harbor detection can be roughly divided into two main directions, one is based on geographic prior information [31,32], and the other is based on feature information [18,27,[33][34][35][36][37][38][39][40][41][42]. These two types of methods both have effective detection performance in some cases, but there are some limitations.…”

“…Feature-based detection methods usually implement harbor detection by extracting key features, which can also be roughly divided into two types. One is based on coastline closure [33][34][35][36][37], and the other is based on wharf features [18,27,[38][39][40][41][42]. The methods based on coastline closure [33][34][35][36][37] are generally designed according to two characteristics, one characteristic is the strong closure of the harbor coastline, and the other is that the contour of the harbor coastline is far longer than the distance of the harbor gate.…”

“…They put forward the idea of detecting transition points to get the positions of changes, which also provides a valuable approach for detecting specific regions in SAR images. Therefore, the methods based on wharf features usually detect the harbors by extracting key points and geometric features of harbors' contours [18,27,[38][39][40][41][42]. The method has a remarkable effect when the wharf has obvious outline characteristics and good straightness characteristics, however, due to the influence of coherent speckle noise and docked ships, these features are often fuzzy in SAR images.…”