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The orthorectified technology for UAV aerial remote sensing image based on the Programmable GPU

Z. Wenhao, L. Yingcheng, L. Delong, T. Changsheng, L. Jin
China TopRS Technology Co., Ltd, Beijing 100039; Chinese Academy of Surveying and Mapping, Beijing 100039; Beijing University of Civil Engineering and Architecture, Beijing 100044, China
IOP Conf. Series: Earth and Environmental Science 17 (2014) 012190

@inproceedings{wenhao2014distortion,

   title={Distortion correction algorithm for UAV remote sensing image based on CUDA},

   author={Wenhao, Zhang and Yingcheng, Li and Delong, Li and Changsheng, Teng and Jin, Liu},

   booktitle={IOP Conference Series: Earth and Environmental Science},

   volume={17},

   number={1},

   pages={012190},

   year={2014},

   organization={IOP Publishing}

}

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The increasingly heterogeneous modern hardware landscape is forcing database vendors to rethink basic design decisions: With more and more architectures to support, the traditional approach of building on hand-tuned operators might simply become too cost- and labor-intensive. With this problem in mind, we introduced the notion of a hardware-oblivious database engine, which avoids device-specific optimizations and targets multiple different hardware architectures from a single code-base. We demonstrated the feasibility of this concept through Ocelot, a prototypical hardware-oblivious database that uses OpenCL to provide operators that can run on multiple architectures. In this demonstration, we show how we modified Ocelot to support self-learning algorithm adaptivity: The ability to automatically learn which algorithms are optimal for a given operation on a given hardware architecture. We present how to specify operators that can be executed by multiple algorithms, provide details about the underlying learning and decision routines, and demonstrate how our system picks the optimal algorithm when running on systems with multiple devices, such as CPUs and graphics cards.
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