Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B6, 123-128, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B6-123-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
27 Jul 2012
A STUDY ON AUTOMATIC UAV IMAGE MOSAIC METHOD FOR PAROXYSMAL DISASTER
M. Li1, D. Li1, and D. Fan2 1School of Remote Sensing and Information Engineering, Wuhan University, NO.129, Luoyu Road, Wuhan 430079, China
2The 3rd Railway Survey & Design Institute, NO.10, Zhongshan Road, Tianjin 300142, China
Keywords: Disaster Information, Image Mosaic, Image Matching, Bundle Adjustment, Dynamic Programming, Registration Abstract. As everyone knows, some paroxysmal disasters, such as flood, can do a great damage in short time. Timely, accurate, and fast acquisition of sufficient disaster information is the prerequisite facing with disaster emergency. Due to UAV's superiority in acquiring disaster data, UAV, a rising remote sensed data has gradually become the first choice for departments of disaster prevention and mitigation to collect the disaster information at first hand. In this paper, a novel and fast strategy is proposed for registering and mosaicing UAV data. Firstly, the original images will not be zoomed in to be 2 times larger ones at the initial course of SIFT operator, and the total number of the pyramid octaves in scale space is reduced to speed up the matching process; sequentially, RANSAC(Random Sample Consensus) is used to eliminate the mismatching tie points. Then, bundle adjustment is introduced to solve all of the camera geometrical calibration parameters jointly. Finally, the best seamline searching strategy based on dynamic schedule is applied to solve the dodging problem arose by aeroplane's side-looking. Beside, a weighted fusion estimation algorithm is employed to eliminate the "fusion ghost" phenomenon.
Conference paper (PDF, 581 KB)


Citation: Li, M., Li, D., and Fan, D.: A STUDY ON AUTOMATIC UAV IMAGE MOSAIC METHOD FOR PAROXYSMAL DISASTER, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B6, 123-128, https://doi.org/10.5194/isprsarchives-XXXIX-B6-123-2012, 2012.

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