An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery
AbstractThis paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC), which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3), Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results. View Full-Text
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Ling, X.; Zhang, Y.; Xiong, J.; Huang, X.; Chen, Z. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery. Remote Sens. 2016, 8, 672.
Ling X, Zhang Y, Xiong J, Huang X, Chen Z. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery. Remote Sensing. 2016; 8(8):672.Chicago/Turabian Style
Ling, Xiao; Zhang, Yongjun; Xiong, Jinxin; Huang, Xu; Chen, Zhipeng. 2016. "An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery." Remote Sens. 8, no. 8: 672.
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