Stream-computing Based High Accuracy On-board Real-time Cloud Detection for High Resolution Optical Satellite Imagery

• Main Authors: WANG Mi, ZHANG Zhiqi, DONG Zhipeng, JIN Shuying, Hongbo SU
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2018-06-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: machine vision; intelligent photogrammetry; cloud detection; stream computing; on-board real-time processing
• Online Access: http://html.rhhz.net/CHXB/html/2018-6-760.htm
• ISSN: 1001-1595; 1001-1595

This paper focuses on the time efficiency for machine vision and intelligent photogrammetry,especially high accuracy on-board real-time cloud detection method.With the development of technology,the data acquisition ability is growing continuously and the volume of raw data is increasing explosively.Meanwhile,because of the higher requirement of data accuracy,the computation load is also become heavier.This situation makes time efficiency extremely important.Moreover,the cloud cover rate of optical satellite imagery is up to approximately 50%,which is seriously restricting the applications of on-board intelligent photogrammetry services.To meet the on-board cloud detection requirements and offer valid input data to subsequent processing,this paper presents a stream-computing based high accuracy on-board real-time cloud detection solution which follows the “bottom-up” understanding strategy of machine vision and uses multiple embedded GPU with significant potential to be applied on-board.Without external memory,the data parallel pipeline system based on multiple processing modules of this solution could afford the “stream-in,processing,stream-out” real-time stream computing.In experiments,images of GF-2 satellite are used to validate the accuracy and performance of this approach,and the experimental results show that this solution could not only bring up cloud detection accuracy,but also match the on-board real-time processing requirements.