Geo-registration and mosaic of UAV video for quick-response to forest fire disaster

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.

[1]  Stephen Hsu,et al.  Geocoded terrestrial mosaics using pose sensors and video registration , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[3]  Dieter Fritsch,et al.  Direct Geocoding - is Aerial Triangulation Obsolete? , 1999 .

[4]  K. Jacobsen.  CALIBRATION ASPECTS IN DIRECT GEOREFERENCING OF FRAME IMAGERY , 2002 .

[5]  Harpreet S. Sawhney,et al.  Registration of video to geo-referenced imagery , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[6]  Jun Wu,et al.  Real-Time UAV Video Processing for Quick- Response to Natural Disaster , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[7]  Richard P. Wildes,et al.  Video georegistration: algorithm and quantitative evaluation , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Anand C. Shastry,et al.  AIRBORNE VIDEO REGISTRATION FOR VISUALIZATION AND PARAMETER ESTIMATION OF TRAFFIC FLOWS , 2002 .

[9]  Suman Srinivasan,et al.  Airborne traffic surveillance systems: video surveillance of highway traffic , 2004, VSSN '04.

[10]  Jan Skaloud.  Direct Versus Indirect Methods of Sensor Orientation , 2000 .

[11]  David R Fletcher.  Spatial Information Technologies in Critical Infrastructure Protection: A Research Agenda in CIP. The Role of Geospatial Technology in Critical Transportation Infrastructure Protection: A Research Agenda , 2002 .

[12]  Michael Moore,et al.  Issues concerning the implementation of a low cost attitude solution for an unmanned airborne vehicle (UAV) , 2003 .

[13]  H. Eisenbeiss.  A MINI UNMANNED AERIAL VEHICLE (UAV): SYSTEM OVERVIEW AND IMAGE ACQUISITION , 2004 .

[14]  Supun Samarasekera,et al.  Registration of highly-oblique and zoomed in aerial video to reference imagery , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  Dorota A. Grejner-Brzezinska.  DIRECT EXTERIOR ORIENTATION OF AIRBORNE IMAGERY WITH GPS/INS SYSTEM: PERFORMANCE ANALYSIS. , 1999 .