Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/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
![]() |
ISSN | 0924-2716 |
EISSN | 1872-8235 |
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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。