Share Email Print
cover

Proceedings Paper

A CPU/GPU collaborative approach to high-speed remote sensing image rectification based on RFM
Author(s): Yiwei Sun; Bin Liu; Xiliang Sun; Wenhui Wan; Kaichang Di; Zhaoqin Liu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

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
Show Author Affiliations
Yiwei Sun, Institute of Remote Sensing Applications (China)
Bin Liu, Institute of Remote Sensing Applications (China)
Xiliang Sun, Institute of Remote Sensing Applications (China)
Wenhui Wan, Institute of Remote Sensing Applications (China)
Kaichang Di, Institute of Remote Sensing Applications (China)
Zhaoqin Liu, Institute of Remote Sensing Applications (China)


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)

© SPIE. Terms of Use
Back to Top