高分辨率光学卫星影像高精度在轨实时云检测的流式计算
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摘要
本文重点阐述基于机器视觉的智能摄影测量的效率基础问题之二:高精度影像在轨实时云检测方法。随着技术发展,数据获取能力不断提升,待处理的数据量呈爆炸式增长;同时,对处理精度需求的提升,导致所需计算量的不断增长,二者凸显了智能摄影测量面临的效率问题。对光学卫星影像而言,高达50%的平均云覆盖率严重制约了高效精准在轨智能摄影测量的实现。针对于此,本文结合机器视觉中"自底向上"的图像理解控制策略,提出一种可供借鉴的基于流式计算的高分辨率光学卫星影像高精度在轨实时云检测方法,采用适合在轨搭载的嵌入式GPU实现实时流式计算,为后续的智能摄影测量处理提供输入。本文方法采用不依赖外存的快速处理机制,对持续流入的数据实时分块,通过负载均衡机制将数据块依次分发至各个单元并行处理,从而实现"流入、处理、流出"的实时处理。利用高分二号数据对本文方法进行试验验证,结果表明本文方法在显著提高云覆盖区域检测精度的同时,综合加速比达14,可满足在轨实时处理需求。
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.
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.
引文
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[21]董志鹏,梅小明,陈杰,等.简单线性迭代聚类的高分辨率遥感影像分割[J].遥感信息,2016,31(6):83-88.DONG Zhipeng,MEI Xiaoming,CHEN Jie,et al.Image Segmentation of High Resolution Remote Sensing Imagery Based on Simple Linear Iterative Clustering[J].Remote Sensing Information,2016,31(6):83-88.
[22]FANG Liuyang,WANG Mi,LI Deren,et al.CPU/GPU Near Real-time Preprocessing for ZY-3Satellite Images:Relative Radiometric Correction,MTF Compensation,and Geocorrection[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,87:229-240.
[23]CHAPMAN B,JOST G,VAN DER PAS R.Using OpenMP:Portable Shared Memory Parallel Programming[M].Cambridge:MIT Press,2007.
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[26]谭凯,张永军,童心,等.国产高分辨率遥感卫星影像自动云检测[J].测绘学报,2016,45(5):581-591.DOI:10.11947/j.AGCS.2016.20150500.TAN Kai,ZHANG Yongjun,TONG Xin,et al.Automatic Cloud Detection for Chinese High Resolution Remote Sensing Satellite Imagery[J].Acta Geodaetica et Cartographica Sinica,2016,45(5):581-591.DOI:10.11947/j.AGCS.2016.20150500.
[2]李德仁,沈欣,龚健雅,等.论我国空间信息网络的构建[J].武汉大学学报(信息科学版),2015,40(6):711-715,766.LI Deren,SHEN Xin,GONG Jianya,et al.On Construction of China’s Space Information Network[J].Geomatics and Information Science of Wuhan University,2015,40(6):711-715,766.
[3]张兵.智能遥感卫星系统[J].遥感学报,2011,15(3):415-431.ZHANG Bing.Intelligent Remote Sensing Satellite System[J].Journal of Remote Sensing,2011,15(3):415-431.
[4]李德仁,王密,沈欣,等.从对地观测卫星到对地观测脑[J].武汉大学学报(信息科学版),2017,42(2):143-149.LI Deren,WANG Mi,SHEN Xin,et al.From Earth Observation Satellite to Earth Observation Brain[J].Geomatics and Information Science of Wuhan University,2017,42(2):143-149.
[5]DAVIS C O,HORAN D M,CORSON M R.On-orbit Calibration of the Naval EarthMap Observer(NEMO)Coastal Ocean Imaging Spectrometer(COIS)[C]∥Proceedings Volume 4132,Imaging Spectrometry VI.San Diego,CA:SPIE,2000:250-259.
[6]VISSER S J,DAWOOD A S.Real-time Natural Disasters Detection and Monitoring from Smart Earth Observation Satellite[J].Journal of Aerospace Engineering,2004,17(1):10-19.
[7]HUANG Jingjin,ZHOU Guoqing.On-board Detection and Matching of Feature Points[J].Remote Sensing,2017,9(6):601.
[8]WU Yuanfeng,GAO Lianru,ZHANG Bing,et al.Embedded GPU Implementation of Anomaly Detection for Hyperspectral Images[C]∥Proceedings of SPIE 9646,Highperformance Computing in Remote Sensing V.Toulouse,France:SPIE,2015:964608.
[9]侯舒维,孙文方,郑小松.遥感图像云检测方法综述[J].空间电子技术,2014,11(3):68-76,86.HOU Shuwei,SUN Wenfang,ZHENG Xiaosong.Overview of Cloud Detection Methods in Remote Sensing Images[J].Space Electronic Technology,2014,11(3):68-76,86.
[10]DINGUIRARD M,SLATER P N.Calibration of Space-multispectral Imaging Sensors:a Review[J].Remote Sensing of Environment,1999,68(3):194-205.
[11]DINGUIRARD M,HENRY P.Calibration of SPOT HRV Cameras[J].Remote Sensing of Environment,1993.
[12]GELLMAN D I,BIGGAR S F,DINGUIRARD M C,et al.Review of SPOT-1and-2Calibrations at White Sands from Launch to the Present[C]∥Proceedings Volume 1938,Recent Advances in Sensors,Radiometric Calibration,and Processing of Remotely Sensed Data.Orlando,FL:SPIE,1993:118-125.
[13]PAHLEVAN N,SCHOTT J R.Characterizing the Relative Calibration of Landsat-7(ETM+)Visible Bands with Terra(MODIS)over Clear Waters:the Implications for Monitoring Water Resources[J].Remote Sensing of Environment,2012,125:167-180.
[14]潘志强,顾行发,刘国栋,等.基于探元直方图匹配的CBERS-01星CCD数据相对辐射校正方法[J].武汉大学学报(信息科学版),2005,30(10):925-927.PAN Zhiqiang,GU Xingfa,LIU Guodong,et al.Relative Radiometric Correction of CBERS-01CCD Data Based on Detector Histogram Matching[J].Geomatics and Information Science of Wuhan University,2005,30(10):925-927.
[15]YI Lina,Zhang Guifeng,WU Zhaocong.A Scale-Synthesis Method for High Spatial Resolution Remote Sensing Image Segmentation[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(10):4062-4070.
[16]LI Deren,ZHANG Guifeng,WU Zhaocong,et al.An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation[J].IEEE Transactions on Image Processing,2010,19(10):2781-2787.
[17]REN X,MALIK J.Learning A Classification Model for Segmentation[C]∥Proceedings of the 9th IEEE International Conference on Computer Vision.Nice,France:IEEE,2003:10-17.
[18]VINCENT L,SOILLE P.Watersheds in Digital Spaces:An Efficient Algorithm Based on Immersion Simulations[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(6):583-598.
[19]FELZENSZWALB P F,HUTTENLOCHER D P.Efficient Graph-based Image Segmentation[J].International Journal of Computer Vision,2004,59(2):167-181.
[20]ACHANTA R,SHAJI A,SMITH K,et al.SLIC Superpixels Compared to State-of-the-art Superpixel Methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[21]董志鹏,梅小明,陈杰,等.简单线性迭代聚类的高分辨率遥感影像分割[J].遥感信息,2016,31(6):83-88.DONG Zhipeng,MEI Xiaoming,CHEN Jie,et al.Image Segmentation of High Resolution Remote Sensing Imagery Based on Simple Linear Iterative Clustering[J].Remote Sensing Information,2016,31(6):83-88.
[22]FANG Liuyang,WANG Mi,LI Deren,et al.CPU/GPU Near Real-time Preprocessing for ZY-3Satellite Images:Relative Radiometric Correction,MTF Compensation,and Geocorrection[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,87:229-240.
[23]CHAPMAN B,JOST G,VAN DER PAS R.Using OpenMP:Portable Shared Memory Parallel Programming[M].Cambridge:MIT Press,2007.
[24]KIRK D B,HWU W M W.Programming Massively Parallel Processors:A Hands-on Approach[M].3rd ed.San Francisco,CA:Morgan Kaufmann,2016.
[25]NVIDIA JETSON.A Little Goes A Long Way[EB/OL].[2017-07-31].http:∥www.nvidia.com/object/embeddedsystems-dev-kits-modules.html.
[26]谭凯,张永军,童心,等.国产高分辨率遥感卫星影像自动云检测[J].测绘学报,2016,45(5):581-591.DOI:10.11947/j.AGCS.2016.20150500.TAN Kai,ZHANG Yongjun,TONG Xin,et al.Automatic Cloud Detection for Chinese High Resolution Remote Sensing Satellite Imagery[J].Acta Geodaetica et Cartographica Sinica,2016,45(5):581-591.DOI:10.11947/j.AGCS.2016.20150500.