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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 49-54     DOI: 10.6046/gtzyyg.2020.03.07
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Different remote sensing image matching methods based on multiple constraints
Bai1(), Yuying2, Chengling3, Yanru2, Shihu1
1. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
2. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083,China
3. Beijing GEOWAY Software Co., Ltd., Beijing 100043, China
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Abstract  

In order to solve the problem that there are large geometric deformation and gray difference between different remote sensing satellite image and it is difficult to match a large number of feature points, the authors put forward a multi-source remote sensing image matching method under multiple constraints in this paper. First, ASIFT algorithm is used to extract high-quality feature points and complete the initial matching, and the matching results are optimized by RANSAC algorithm. Secondly, affine transformation matrix of the two images is calculated by using the matching feature points set, and the remaining feature points are matched again by combining affine transformation and gray correlation coefficient. Finally, support vector regression (SVR) is used to check the matching results. Satellite images of ZY3-01, ZY3-02 and GF-1 were selected in the experiment. The experimental results show that, compared with SIFT and ASIFT algorithms, the proposed method can greatly increase the number of matching points between multi-source remote sensing images and improve the matching accuracy.
Keywords multi-source remote sensing images      image matching      ASIFT      affine transformation      support vector regression     
:  TP79  
Issue Date: 09 October 2020
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Bai
Yuying
Chengling
Yanru
Shihu
Cite this article:   
Bai,Yuying,Chengling, et al. Different remote sensing image matching methods based on multiple constraints[J]. Remote Sensing for Land & Resources, 2020, 32(3): 49-54.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.03.07     OR     https://www.gtzyyg.com/EN/Y2020/V32/I3/49
Fig.1  The matching flow of the proposed method
序号 传感器 波段 图像大
小/像素		 空间分辨率/m
实验一 ZY3-01 全色正视 583×502 2.1
ZY3-02 全色正视 583×502 2.1
实验二 ZY3-02 全色正视 597×534 2.1
ZY3-02 多光谱 219×198 5.8
实验三 GF-1 全色正视 500×500 2.0
ZY3-01 全色正视 500×500 2.1
Tab.1  Experiment image parameters
序号 算法 特征点个数 匹配对数
参考影像 待匹配影像
实验一 SIFT 3 670 2 993 259
ASIFT 35 100 33 954 374
本文方法 35 100 33 954 1 865
实验二 SIFT 475 3 704 121
ASIFT 31 973 25 443 355
本文方法 31 973 25 443 2 838
实验三 SIFT 2 093 3 530 2
ASIFT 23 426 37 589 21
本文方法 23 426 37 589 61
Tab.2  Results of SIFT, ASIFT and the proposed method
Fig.2-1  Different remote sensing image matching results
Fig.2-2  Different remote sensing image matching results
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