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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 123-128     DOI: 10.6046/gtzyyg.2020143
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An automatic registration algorithm for remote sensing images based on grid index
ZHANG Mengsheng1,2,3(), YANG Shuwen1,2,3(), JIA Xin1,2,3, ZANG Liri1,2,3
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Gansun Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
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Abstract  

This paper proposes an algorithm for automatic registration of remote sensing images based on grid index, aiming at tackling the problems of a small number of registration point pairs and a large number of mismatches captured by the SIFT algorithm in the process of remote sensing image registration. First, SIFT algorithm is used to extract feature points and feature vectors, and matching is made by Euclidean distance; secondly, a grid index is established to eliminate part of the mismatched point pairs, thereby improving the accuracy of the random sampling consensus algorithm; finally, geometric polynomials are used to achieve accurate registration of remote sensing images. The experimental results show that the algorithm has higher accuracy of matching point pairs than the traditional block algorithm in remote sensing images, and takes into account the differences in registration scenes of different remote sensing images.
Keywords SIFT      grid index      RANSAC      image registration     
ZTFLH:  P237  
Corresponding Authors: YANG Shuwen     E-mail: 642722474@qq.com;ysw040966@163.com
Issue Date: 18 March 2021
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Mengsheng ZHANG
Shuwen YANG
Xin JIA
Liri ZANG
Cite this article:   
Mengsheng ZHANG,Shuwen YANG,Xin JIA, et al. An automatic registration algorithm for remote sensing images based on grid index[J]. Remote Sensing for Land & Resources, 2021, 33(1): 123-128.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020143     OR     https://www.gtzyyg.com/EN/Y2021/V33/I1/123
Fig.1  SIFT feature points
Fig.2  Result of matching by SIFT algorithm
Fig.3  Remote sensing image extraction feature points to establish grid index
Fig.4  Flow chart of remote sensing image registration based on grid index
编号 待配准影像 基准影像 影像特点
卫星传感器 空间分
辨率/m		 大小/像素 卫星传感器 空间分
辨率/m		 大小/像素
1 GF-1 PMS全色 2 1 213×866 GF-1 PMS融合 2 1 640×1 247 地形平坦
2 GF-1 PMS全色 2 1 854×1 660 GF-1 PMS融合 2 2 437×2 103 地形起伏较大
3 GF-2 PMS多光谱 4 1 077×1 025 GF-1 PMS融合 2 2 916×2 755 分辨率差异大、拍摄卫星不同
Tab.1  Detailed description of test data
Fig.5  Test data
编号 SIFT SIFT+RANSAC 分块SIFT 本文算法
数量/个 正确率/% 数量/个 正确率/% 数量/个 正确率/% 数量/个 正确率/%
1 302 66.23 242 82.64 1 234 65.64 824 98.30
2 543 73.66 445 89.89 2 337 60.98 1 457 97.80
3 117 42.74 71 70.42 378 34.92 141 93.62
Tab.2  Comparison of the number and correct rate of registration of four algorithms
Fig.6  Image registration results
点号 配准结果 参考影像 误差
X坐标 Y坐标 X坐标 Y坐标
1 388 878.864 2 3 991 408.902 2 388 877.898 1 3 991 409.868 6 1.867 278 171
2 387 901.443 2 3 991 468.957 5 387 902.409 5 3 991 469.440 7 1.167 217 930
3 387 173.594 8 3 991 709.204 0 387 172.990 8 3 991 710.411 9 1.823 838 411
4 386 640.921 4 3 991 768.720 2 386 639.471 9 3 991 768.237 0 2.334 532 490
5 388 840.743 0 3 992 224.246 2 388 842.675 8 3 992 223.279 9 4.669 451 530
6 387 287.925 7 3 992 392.608 9 387 289.375 3 3 992 394.058 5 4.202 680 320
7 386 452.627 8 3 992 431.072 6 386 453.594 1 3 992 432.662 3 3.460 881 780
8 388 841.030 2 3 992 224.250 7 388 841.803 1 3 992 223.477 6 1.195 058 021
9 388 269.212 1 3 992 859.006 4 388 268.796 8 3 992 860.314 0 1.882 291 850
10 387 464.323 5 3 991 903.360 7 387 464.323 5 3 991 905.092 5 2.999 131 239
Tab.3  Registration accuracy evaluation
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