Image registration approach with scale-invariant feature transform algorithm and tangent-crossing-point feature

被引：3
|
作者：
Song, Zhili [1 ]
Zhang, Jiaqi [1 ]
机构：
[1] Shanghai Inst Technol, Sch Comp Sci & Informat Engn, Shanghai, Peoples R China
来源：
JOURNAL OF ELECTRONIC IMAGING | 2020年 / 29卷 / 02期
关键词：
image registration; remote sensing; scale-invariant feature transform; tangent-crossing-point feature; curve matching algorithm; LOCAL FEATURE DESCRIPTOR;
D O I：
10.1117/1.JEI.29.2.023010
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Due to considerable diversities of multimodality remote sensing images in spectral component, the performances of scale-invariant feature transform (or SIFT) may be problematic. In view of this, a model of image registration based on tangent-crossing-point feature is proposed. With the help of tangent-cross-point feature, verifying the correctness of the feature matching is very easy, since it adopts the location information indexed by the correct matching pair of feature-points and transformation information determined by the correct matching pair of curves simultaneously. Experimental results show that this method is more efficient and reliable than the classic SIFT algorithm in some cases. (C) 2020 SPIE and IS&T
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页数：14
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