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.

Date Published: 14 May 2014
PDF: 8 pages
Proc. SPIE 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 91580F (14 May 2014); doi: 10.1117/12.2063894

Published in SPIE Proceedings Vol. 9158:
Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China
Qingxi Tong; Jie Shan; Boqin Zhu, Editor(s)