ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 19-26, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/19/2016/
doi:10.5194/isprs-annals-III-3-19-2016
 
02 Jun 2016
ROBUST LOW-ALTITUDE IMAGE MATCHING BASED ON LOCAL REGION CONSTRAINT AND FEATURE SIMILARITY CONFIDENCE
Min Chen1,2,3, Qing Zhu1,2,3, Shengzhi Huang1,2,3, Han Hu1, and Jingxue Wang1 1Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756, China
2State-province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Chengdu, 610031, China
3Collaborative Innovation Center for Rail Transport Safety, Chengdu, 610031, China
Keywords: Low-altitude remote sensing, Image matching, Local region, Feature similarity confidence Abstract. Improving the matching reliability of low-altitude images is one of the most challenging issues in recent years, particularly for images with large viewpoint variation. In this study, an approach for low-altitude remote sensing image matching that is robust to the geometric transformation caused by viewpoint change is proposed. First, multiresolution local regions are extracted from the images and each local region is normalized to a circular area based on a transformation. Second, interest points are detected and clustered into local regions. The feature area of each interest point is determined under the constraint of the local region which the point belongs to. Then, a descriptor is computed for each interest point by using the classical scale invariant feature transform (SIFT). Finally, a feature matching strategy is proposed on the basis of feature similarity confidence to obtain reliable matches. Experimental results show that the proposed method provides significant improvements in the number of correct matches compared with other traditional methods.
Conference paper (PDF, 1382 KB)


Citation: Chen, M., Zhu, Q., Huang, S., Hu, H., and Wang, J.: ROBUST LOW-ALTITUDE IMAGE MATCHING BASED ON LOCAL REGION CONSTRAINT AND FEATURE SIMILARITY CONFIDENCE, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 19-26, doi:10.5194/isprs-annals-III-3-19-2016, 2016.

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