A CPU/GPU collaborative approach to high-speed remote sensing image rectification based on RFM

Yiwei Sun; Bin Liu; Xiliang Sun; Wenhui Wan; Kaichang Di; Zhaoqin Liu

doi:10.1117/12.2063894

14 May 2014 A CPU/GPU collaborative approach to high-speed remote sensing image rectification based on RFM

Yiwei Sun, Bin Liu, Xiliang Sun, Wenhui Wan, Kaichang Di, Zhaoqin Liu

Proceedings Volume 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China; 91580F (2014) https://doi.org/10.1117/12.2063894
Event: Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 2012, Wuhan, China

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.

Citation Download Citation

Yiwei Sun, Bin Liu, Xiliang Sun, Wenhui Wan, Kaichang Di, and Zhaoqin Liu "A CPU/GPU collaborative approach to high-speed remote sensing image rectification based on RFM", Proc. SPIE 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 91580F (14 May 2014); https://doi.org/10.1117/12.2063894

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available