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DOI10.1016/j.isprsjprs.2017.04.015
Poor textural image tie point matching via graph theory
Yuan, Xiuxiao1,2; Chen, Shiyu1; Yuan, Wei1,3; Cai, Yang1
通讯作者Yuan, Wei
来源期刊ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
EISSN1872-8235
出版年2017
卷号129页码:21-31
英文摘要

Feature matching aims to find corresponding points to serve as tie points between images. Robust matching is still a challenging task when input images are characterized by low contrast or contain repetitive patterns, occlusions, or homogeneous textures. In this paper, a novel feature matching algorithm based on graph theory is proposed. This algorithm integrates both geometric and radiometric constraints into an edge-weighted (EW) affinity tensor. Tie points are then obtained by high-order graph matching. Four pairs of poor textural images covering forests, deserts, bare lands, and urban areas are tested. For comparison, three state-of-the-art matching techniques, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), and features from accelerated segment test (FAST), are also used. The experimental results show that the matching recall obtained by SIFT, SURF, and FAST varies from 0 to 35% in different types of poor textures. However, through the integration of both geometry and radiometry and the EW strategy, the recall obtained by the proposed algorithm is better than 50% in all four image pairs. The better matching recall improves the number of correct matches, dispersion, and positional accuracy. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.


英文关键词Tie point matching Poor textural image Affinity tensor Edge-weighted High-order graph matching
类型Article
语种英语
国家Peoples R China ; Japan
收录类别SCI-E
WOS记录号WOS:000403860600003
WOS关键词DESCRIPTOR
WOS类目Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/199904
作者单位1.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China;
2.Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China;
3.Univ Tokyo, Ctr Spatial Informat Sci, Kashiwa, Chiba 2778568, Japan
推荐引用方式
GB/T 7714
Yuan, Xiuxiao,Chen, Shiyu,Yuan, Wei,et al. Poor textural image tie point matching via graph theory[J],2017,129:21-31.
APA Yuan, Xiuxiao,Chen, Shiyu,Yuan, Wei,&Cai, Yang.(2017).Poor textural image tie point matching via graph theory.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,129,21-31.
MLA Yuan, Xiuxiao,et al."Poor textural image tie point matching via graph theory".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 129(2017):21-31.
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