Airport Detection From Large IKONOS Images Using Clustered SIFT Keypoints and Region Information

作者：Tao Chao^*; Tan Yihua; Cai Huajie; Tian Jinwen

来源：IEEE Geoscience and Remote Sensing Letters, 2011, 8(1): 128-132.

DOI：10.1109/LGRS.2010.2051792

• Airport detection
• clustering
• high spatial resolution
• scale-invariant feature transform (SIFT)

This letter presents a new method for airport detection from large high-spatial-resolution IKONOS images. To this end, we describe airport by a set of scale-invariant feature transform (SIFT) keypoints and detect it using an improved SIFT matching strategy. After obtaining SIFT matched keypoints, to both discard the redundant matched points and locate the possible regions of candidates that contain the target, a novel region-location algorithm is proposed, which exploits the clustering information from matched SIFT keypoints, as well as the region information extracted through the image segmentation. Finally, airport recognition is achieved by applying the prior knowledge to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.

• 出版日期2011-1
• 单位华中科技大学