Urban green space remote sensing image registration using image mixed features

doi:10.3785/j.issn.1008-973X.2019.06.021

Journal of ZheJiang University (Engineering Science)

2019, Vol. 53

Issue (6): 1205-1217 DOI: 10.3785/j.issn.1008-973X.2019.06.021

Computer and Aut omation Technology

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

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Abstract

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.

Key words： remote sensing image registration multiple image features non-rigid distortion multi view multi temporal

Received: 24 April 2018 Published: 22 May 2019

CLC:

TP 75

Corresponding Authors: Yang YANG E-mail: gxy0415@163.com;yyang_ynu@163.com

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Cite this article:

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.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.06.021 OR http://www.zjujournals.com/eng/Y2019/V53/I6/1205

基于图像混合特征的城市绿地遥感图像配准

为了解决同一场景城市绿地遥感图像因视角变化等原因不在同一坐标系，以致于不能对其进行变化检测的问题，提出一种基于图像混合特征的遥感图像配准方法. 1）提取SIFT特征点:从待配准图像和参考图像提取足够的SIFT特征点；2）基于混合特征的SIFT特征点配准：首先在特征点集Y和X之间进行对应关系评估，然后利用对应关系建立空间映射函数不断更新形变后源点集的位置；3）图像配准：基于源点集和形变后的源点集来构造一个映射函数，从而对图像进行配准. 在与当前流行的4种算法（SIFT、CPD、RSOC、GLMDTPS）的对比实验中，提出的算法均给出了精确的配准结果，在大部分实验中其性能超过了其他算法.

关键词： 遥感图像配准, 图像混合特征, 非刚性畸变, 多视角, 多时相

Fig.1 Flow chart of remote sensing image registration algorithm

Fig.2 Local fragment formed by center point and its nearest five neighboring points form small fragment in M-shaped feature point set

Fig.3 Correspondence illustration obtained under different velocity field constraints

Fig.4 Registration performance comparison of single feature and mixed features

Fig.5 Registration performance demonstration using different mixture features (proposed algorithm and GLMDTPS)

Fig.6 Robustness comparison between L₂E and MLE under interference of different numbers of outliers

Fig.7 Experimental results display of parameters β and ε

Tab.1 Datasets of remote sensing image registration experiments

Tab.2 Experiment results of satellite remote sensing image registration (test data: dataset Ⅰ)

Fig.8 Examples of satellite remote sensing image registration results for typical three sets in dataset I

Tab.3 Experiment results of low-altitude remote sensing image registration for small unmanned aerial vehicles (SUAV) (test data: dataset II)

Fig.9 Examples of SUAV remote sensing image registration results for typical three sets in dataset II

Tab.4 Experimental results on satellite remote sensing images

Fig.10 Registration 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)

Tab.5 Experimental results on UAV remote sensing images

Fig.11 Registration 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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