With its fast development,the unmanned aerial vehicle (UAV) technology has become an important method for obtaiing the remote sensing image data. Nevertheless, this flexibility,rapid acquisition method for remote sensing image has poor stability in the platform in comparison with the traditional way of large aircraft aerial potography. The acquisition process of UAV image is affected by its counterweight,real-time flight environment and other external factors,and all of these factors lead to a host of difficulties in image registration. In this paper,firstly,the authors used the POS data to estimate the overlapped area of the UAV image,utilized the Forstner operator to extract feature points,and segmented the images based on the entropy information. After that, the matching feature points were found with the rotation model based on normalized cross-correlation(NCC). Finally, the registration of the UAV images was realized. The experimental results show that the method proposed in this paper is effective and maintains a better robustness.
贺一楠, 耿娟, 秦军, 刘晨, 杨辉. 结合Forstner与NCC约束的UAV图像配准研究[J]. 国土资源遥感, 2015, 27(1): 48-54.
HE Yinan, GENG Juan, QIN Jun, LIU Chen, YANG Hui. Integration of Forstner and NCC constraint for UAV image registration. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 48-54.
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2015, Vol. 27 
