Methods of orientating different resolution satellite images with the existing vector maps

Luping Lu; Yong Zhang; Yongjun Zhang; Zuxun Zhang

doi:10.1117/12.902432

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23 November 2011 Methods of orientating different resolution satellite images with the existing vector maps

Luping Lu, Yong Zhang, Yongjun Zhang, Zuxun Zhang

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Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 80061E (2011) https://doi.org/10.1117/12.902432
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

In this paper, the different methods of orientating different resolution satellite images with the existing vector maps are introduced. To moderate high resolution satellite image, such as SPOT5 image, the vector road maps are often used for the orientation, and the vector lines in the maps usually represent the road central lines. And to higher resolution satellite image, such as QuickBird image, the vector lines in the maps usually represent the road edges, instead. Beside that, the different extend of details of the images makes it necessary to handle them with different methods. Because in very high resolution satellite images a lot of disturbing image features will be extracted along with the wanted one. A voting algorithm is employed to solve the problem, the approach is based on the previous work where an edge-based voting strategy was studied. The voting algorithm has the advantages of globally optimal and robust to noise. It can directly estimate the transformation parameters, meanwhile providing the potential matches of edge points, these matches can then be used in calculating the accurate orientation parameters, and providing the chance of change detection since the unchanged objects can be marked out with this method.

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Luping Lu, Yong Zhang, Yongjun Zhang, and Zuxun Zhang "Methods of orientating different resolution satellite images with the existing vector maps", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80061E (23 November 2011); https://doi.org/10.1117/12.902432

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