A CPU/GPU Collaborative Approach to High-Speed Remote Sensing Image Rectification Based On RFM

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.