Urban green space remote sensing image registration using image mixed features
Xue-yan GAO(),An-ning PAN,Yang YANG*()
School of Information Science and Technology, The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China
A remote sensing image registration method based on image mixed features was proposed in order to solve the problem that the remote sensing images of urban green space in the same scene are not in the same coordinate system due to the change of viewpoint, and the change detection cannot be performed. Firstly, SIFT-based feature point extraction: exact sufficient SIFT feature points from the sensed image and the reference image. Secondly, SIFT feature point registration based on mixed features: the correspondence estimation between the feature point set Y and X, and then the correspondence was used to establish a spatial mapping function to continuously update the position of the transformed source point set. Thirdly, image registration: a mapping function was constructed based on the source point set and the transformed source point set to register the image. The experimental results show that, compared with four popular methods (SIFT, CPD, RSOC, GLMDTPS), the proposed method all gives accurate registration results, even presents better performance than the other methods in most cases.
Xue-yan GAO,An-ning PAN,Yang YANG. Urban green space remote sensing image registration using image mixed features. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1205-1217.
Fig.8Examples of satellite remote sensing image registration results for typical three sets in dataset I
方法
RMSE
MAE
方法
RMSE
MAE
SIFT
?
?
GLMDTPS
5.200 0
9.516 4
CPD
7.299 6
10.203 5
本研究
1.743 8
2.031 3
RSOC
4.524 7
7.228 3
?
?
?
Tab.3Experiment results of low-altitude remote sensing image registration for small unmanned aerial vehicles (SUAV) (test data: dataset II)
Fig.9Examples of SUAV remote sensing image registration results for typical three sets in dataset II
评估方法
RMSE
MAE
a
2.477 6
2.914 4
b
1.516 2
1.986 9
c
1.820 5
1.157 6
d
2.330 1
2.757 1
e
0.982 8
2.240 7
均值
1.507 4
1.611 3
Tab.4Experimental results on satellite remote sensing images
Fig.10Registration examples on five typical satellite remote sensing image pairs (I: sensed images, II: reference images, III: transformed images, IV: 5×5 checkboards for alternately demonstrating transformed and reference images)
评估方法
RMSE
MAE
a
0.990 3
1.240 7
b
1.954 8
2.166 3
c
1.181 2
1.347 92
d
1.688 9
1.968 49
e
1.425 9
1.636 76
均值
1.448 2
1.672 0
Tab.5Experimental results on UAV remote sensing images
Fig.11Registration examples on five typical UAV image pairs (I: sensed images, II: reference images, III: transformed images, IV: 5×5 checkboards for alternately demonstrating transformed and reference images)
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