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Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor

School of Geographic Sciences, Xinyang Normal University, 237 Nanhu Road, Xinyang 464000, China
School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan
Visiontek Research, 6 Phoenix Avenue, Wuhan 430205, China
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1104;
Received: 10 May 2018 / Revised: 14 June 2018 / Accepted: 26 June 2018 / Published: 11 July 2018
(This article belongs to the Special Issue Multisensor Data Fusion in Remote Sensing)
PDF [7789 KB, uploaded 11 July 2018]


Matching multi-sensor remote sensing images is still a challenging task due to textural changes and non-linear intensity differences. In this paper, a novel matching method is proposed for multi-sensor remote sensing images. To establish feature correspondences, an affinity tensor is used to integrate geometric and radiometric information. The matching process consists of three steps. First, features from an accelerated segment test are extracted from both source and target images, and two complete graphs are constructed with their nodes representing these features. Then, the geometric and radiometric similarities of the feature points are represented by the three-order affinity tensor, and the initial feature correspondences are established by tensor power iteration. Finally, a tensor-based mismatch detection process is conducted to purify the initial matched points. The robustness and capability of the proposed method are tested with a variety of remote sensing images such as Ziyuan-3 backward, Ziyuan-3 nadir, Gaofen-1, Gaofen-2, unmanned aerial vehicle platform, and Jilin-1. The experiments show that the average matching recall is greater than 0.5, which outperforms state-of-the-art multi-sensor image-matching algorithms such as SIFT, SURF, NG-SIFT, OR-SIFT and GOM-SIFT. View Full-Text
Keywords: image matching; multi-sensor remote sensing image; graph theory; affinity tensor; matching blunder detection image matching; multi-sensor remote sensing image; graph theory; affinity tensor; matching blunder detection

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Chen, S.; Yuan, X.; Yuan, W.; Niu, J.; Xu, F.; Zhang, Y. Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor. Remote Sens. 2018, 10, 1104.

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