A CPU/GPU collaborative approach to high-speed remote sensing image rectification based on RFM
2014
Image rectification is a common task in remote sensing application and usually time-consuming for large-size images.
Based on the characteristics of the Rational Functional Model (RFM)-based rectification process, this paper proposes a
novel CPU/GPU collaborative approach to high-speed rectification of remote sensing images. Three performance
optimization strategies are presented in detail, including maximizing device occupancy, improving memory access
efficiency and increasing instruction throughput. Experimental results using SPOT-5 and ZiYuan-3 (ZY3) remote
sensing images show that the proposed method can achieve the processing speed up to 8GB/min, which significantly
exceeds that of common commercial software. Real-time remote sensing image rectification can be expected with further
optimized algorithm and more efficient I/O operation.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
1
References
1
Citations
NaN
KQI