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《Acta Geodaetica et Cartographica Sinica》 2018-06
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Stream-computing Based High Accuracy On-board Real-time Cloud Detection for High Resolution Optical Satellite Imagery

WANG Mi;ZHANG Zhiqi;DONG Zhipeng;JIN Shuying;Hongbo SU;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University;Collaborative Innovation Center of Geospatial Technology;Department of Civil,Environmental and Geomatics Engineering,Florida Atlantic University;  
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 approximately50%,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.Inexperiments,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.
【Fund】: 国家自然科学基金(91438203;91638301;91438111;41601476)~~
【CateGory Index】: P237
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