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Proceedings Paper

Geo-registration and mosaic of UAV video for quick-response to forest fire disaster
Author(s): Jun Wu; Zhongkui Dong; Guoqing Zhou
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Paper Abstract

UAV Video is rapidly emerging as a widely used source of imagery for many applications in recent years. This paper presents our research on the UAV video processing system for the purpose of fire surveillance, which include: (1) UAV video stream processing. This step involves three aspects: decoding, re-sampling and matching. Microsoft(R) DirectX(R) technology is used to decode highly compressed video stream and re-sampled them into still video frame based on the time base and rate of UAV navigation sensor. One feature-based image-matching algorithm is developed to quickly obtain Tie points for latter calibration operation. (2) UAV system orientation. This step also involves three aspects: Camera IOP, Boresight Alignment and bundle adjustment. TSAI's two-stage technique is used to obtain initial camera focus length f, distortion coefficients k1 and six Exterior Of Parameter (EOP) for one selected video image. Meanwhile, the Boresight Matrix is deduced by the comparison of GPS/INS derived parameters with solved EOPs. Further more, all parameters including EOPs of all re-sampled video images and camera IOP are optimally estimated based on developed bundle adjustment algorithm. (3) UAV Video geo-registration and mosaic. All re-sampled video frames are geo-registered into uniform geo-reference coordinate frame vice Classic photogrammetric orthorectification model and merge with each other with developed mosaic algorithm. The results demonstrated that the geo-accuracy of mosaic image generated from UAV video can achieve 1-2 pixels in planimetry and its combination with GIS-supported data for fast response to time-critical event, e.g., forest fire, is descried.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678810 (15 November 2007); doi: 10.1117/12.748824
Show Author Affiliations
Jun Wu, Guilin Univ. of Electronic Technology (China)
Zhongkui Dong, China Institute of Geotechnical Investigation and Surveying (China)
Guoqing Zhou, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

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