期刊
IEEE ACCESS
卷 7, 期 -, 页码 177474-177483出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2958658
关键词
Multi-sensor images; dense descriptor; similarity measurement; template matching
资金
- National Natural Science Foundation of China [61802423]
Automatic and reliable multi-sensor image matching is a very challenging task due to the significant nonlinear radiometric differences between multi-sensor images. In this paper, a novel dense descriptor based on adaptive multiscale structure orientation is proposed for capturing the geometrical structure information of an image. The dense descriptor of the proposed matching algorithm is not only illumination and contrast invariant but also robust against the image noise. Further, an improved similarity measurement is introduced for adapting the orientation reversal caused by the intensity inversion between multi-sensor images. Based on the robust dense descriptor and the improved similarity measurement, we developed a novel and practical template matching algorithm to match multi-sensor images reliably. We evaluate the proposed matching algorithm by comparing it with other state-of-the-art algorithms. The experimental results show the proposed algorithm has a significant advantage on matching accuracy.
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