UAV remote sensing image processing and classification in Wenchuan earthquake district

Tao Zeng; Wunian Yang; Hao Wu

doi:10.1117/12.832957

30 October 2009 UAV remote sensing image processing and classification in Wenchuan earthquake district

Tao Zeng, Wunian Yang, Hao Wu

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 749823 (2009) https://doi.org/10.1117/12.832957
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Remote sensing image presented as one of the most effective methods to acquire the disaster information in the Wenchuan earthquake. For its technical advantages such as rapid and flexible takeoff, the Unmanned Aerial Vehicles (UAV) played an important role in the "5.12" Wenchuan earthquake emergency relief. The UAV remote sensing image has some special characteristics contrasting with other RS image, so how to process quickly the images is the key issue. In this paper, a fast method is presented for moisacing the remote sensing images from the UAV without ground control points. The method grounds on two principles, parallel computing and digital photogrammetry. The experimental results justify the needs of uncontrolled mosaic images production for the requirements of post-disaster emergency. The mosaic images without correction by ground control points provide no geographic information, but they can provide panoramic and macro-information of earthquake-stricken areas. Then the geometric correction technique was employed to correct the panoramic mosaic images to obtain the high-precise digital orthophoto based on the pre-disaster information such as topographic maps. By using the object-oriented method for the information extraction and rapid automatic classification of the UVA remote sensing image can achieve the anticipative goals.

Citation Download Citation

Tao Zeng, Wunian Yang, and Hao Wu "UAV remote sensing image processing and classification in Wenchuan earthquake district", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749823 (30 October 2009); https://doi.org/10.1117/12.832957

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