## Materials and Methods
### Harbor Candidate Extraction  
After image preprocessing, we will extract the candidate regions of the harbors. For a given high-resolution broad-area remote-sensing image, the main characteristics of the harbor are as follows:  
1. Overall shape of the harbor region is semi-closed and it contains some strip-shaped targets of a certain length (i.e., the jetty, including the breakwater and the code head); the strip-shaped targets are surrounded by water on both sides;
2. The gray value of water in the remote-sensing image is generally low and the gray value distribution is uniform (the variance is relatively small), while the gray value and variance of the land are larger than those values of the water, so there is a clear boundary between the water and the land;
3. There are more than two jetties in the same harbor, the jetties are adjacent, and two or more jetties are overlapping;
4. The container yard around the dock has obvious aspect ratio and has obvious geometric features.  
In order to use of the above characteristics of the harbor target and increase the extraction efficiency, we have proposed EC-SIFT, which can remove redundant SIFT keypoints to further reduce the required calculations.  
Scale-invariant feature transform is a successful local feature descriptor, which is widely used in template matching of various images. The traditional SIFT algorithm implementation has the following steps: First, extracting SIFT keypoints from the template images and the target images respectively; secondly, describing each SIFT keypoint separately, and it can form a 128-dimensional keypoint vector for each keypoint; finally, the target extraction results are obtained by matching the keypoint vectors of the target images and the template images. However, when the traditional SIFT algorithm is applied to remote-sensing images, there will be a large number of non-target keypoints; moreover, it is difficult to get all types of harbor templates. Therefore, the keypoints matching for harbor targets may be inaccurate, which directly leads to an increase in the false detection rate of harbor extraction results.  
Figure 2: **(a)** The original image. **(b)** The edge detection result of original image by [PERSON]. **(c)** The smoothing result by the bilateral filtering algorithm. **(d)** The edge detection result of Figure 77c by [PERSON]. **(e)** The smoothing result by the general edge-preserve algorithm. **(f)** The edge detection result of Figure 77e by [PERSON]. **(g)** The smoothing result by the guided filtering algorithm. **(h)** The edge detection result of Figure 77g by [PERSON]. **(i)** The smoothing result by the method proposed. **(j)** The edge detection result of Figure 77i by [PERSON].  
In order to solve these problems, we propose EC-SIFT algorithm to reduce the false detection rate and the number of redundant matching keypoints. The EC-SIFT algorithm combines EC feature and clustering information in the SIFT feature extraction and matching process; we can accurately extract and detect complete harbor candidates. In this part of the article, EC-SIFT algorithm can reduce the redundant keypoints of images. Then, under the guidance of the clustering information, we use the QT algorithm to get the complete harbor candidates. The next section will describe these algorithms in detail.