2025年4月6日 6:24 星期日
首页 > 过刊浏览>2018年第10卷第1期 >81-91. DOI:10.13878/j.cnki.jnuist.2018.01.008
PDF HTML阅读 XML下载 导出引用 引用提醒
高光谱遥感图像混合像元分解的群智能算法
作者:
基金项目:

国家自然科学基金（41571349，91638201）


Swarm intelligence algorithms for spectral unmixing in hyperspectral image
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献
    • |
  • 相似文献
    • | | |
  • 文章评论
    摘要:

    近年来，通过群智能算法求解组合优化或连续优化问题以实现高光谱图像混合像元分解方面取得了重要进展和显著成果.本文首先回顾了高光谱图像混合像元分解的研究背景和群智能算法的特点，然后梳理了光谱混合模型及对应的最优化模型，进而介绍了基于群智能算法的端元提取和丰度反演方法，最后通过2组实验比较了群智能算法和其他传统算法在端元提取和丰度反演方面的精度，对基于群智能算法的混合像元分解效果进行了评价.另外，本文也对群智能算法在高光谱图像信息提取中应用的优势和存在的问题进行了总结.

    Abstract:

    In recent years,swarm intelligence algorithms have made important progress and remarkable achievements in spectral unmixing of hyperspectral image by solving combinatorial optimization or continuous optimization problems.In this paper,the background of the research of spectral unmixing in hyperspectral image and the characteristics of swarm intelligence algorithm were reviewed firstly,and then the optimization model and the spectral mixture model were teased out correspondingly.Then the endmember extraction and abundance inversion method based on swarm intelligent algorithms were introduced.Finally the accuracy of spectral unmixing achieved by swarm intelligence algorithms and other traditional algorithms was evaluated through two experiments.In addition,the advantages and problems of swarm intelligence algorithm in hyperspectral image information extraction were also summarized in this paper.

    参考文献
    [1] 童庆禧,张兵,郑兰芬.高光谱遥感:原理技术与应用[M].北京:高等教育出版社,2006 TONG Qingxi,ZHANG Bing,ZHENG Lanfen.Hyperspectral remote sensing:Principle,technology,and applications[M].Beijing:Higher Education Press,2006
    [2] 张兵,孙旭.高光谱图像混合像元分解[M].北京:科学出版社,2015 ZHANG Bing,SUN Xu.Hyperspectral image unmixing[M].Beijing:Science Press,2015
    [3] Shaw G A,Burke H K.Spectral imaging for remote sensing[J].Lincoln Laboratory Journal,2003,14(1):3-28
    [4] 张兵.高光谱图像处理与信息提取前沿[J].遥感学报,2016,20(5):1062-1090 ZHANG Bing.Advancements of hyperspectral image processing and information extraction[J].Journal of Remote Sensing,2016,20(5):1062-1090
    [5] Bioucas-Dias J M,Plaza A,Dobigeon N,et al.Hyperspectral unmixing overview:Geometrical,statistical,and sparse regression-based approaches[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2012,5(2):354-379
    [6] Dobigeon N,Tourneret J Y,Richard C,et al.Nonlinear unmixing of hyperspectral images:Models and algorithms[J].IEEE Signal Processing Magazine,2014,31(1):82-94
    [7] Raksuntorn N,Du Q A.Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment[J].IEEE Geoscience and Remote Sensing Letters,2010,7(4):836-840
    [8] Fan W Y,Hu B X,Miller J,et al.Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data[J].International Journal of Remote Sensing,2009,30(11):2951-2962
    [9] Halimi A,Altmann Y,Dobigeon N,et al.Nonlinear unmixing of hyperspectral images using a generalized bilinear model[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(11):4153-4162
    [10] Eches O,Dobigeon N,Tourneret J Y.Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(3):582-591
    [11] Blum C,Merkle D.Swarm intelligence introduction and applications[M].Berlin:Springer,2008:43-85
    [12] Zhang B,Sun X,Gao L R,et al.Endmember extraction of hyperspectral remote sensing images based on the Ant Colony Optimization (ACO) algorithm[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(7):2635-2646
    [13] Zhang B,Sun X,Gao L R,et al.Endmember extraction of hyperspectral remote sensing images based on the discrete particle swarm optimization algorithm[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(11):4173-4176
    [14] Zhang B,Gao J W,Gao L R,et al.Improvements in the ant colony optimization algorithm for endmember extraction from hyperspectral images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2013,6(2):522-530
    [15] Gao L R,Zhuang L N,Wu Y F,et al.A quantitative and comparative analysis of different preprocessing implementations of DPSO:A robust endmember extraction algorithm[J].Soft Computing,2014,20(12):1-15
    [16] Sun X,Yang L N,Zhang B,et al.An endmember extraction method based on artificial bee colony algorithms for hyperspectral remote sensing images[J].Remote Sensing,2015,7(12):16363-16383
    [17] Su Y C,Sun X,Gao L R,et al.Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images[J].Journal of Applied Remote Sensing,2016,10(4):45018
    [18] Luo W F,Gao L R,Plaza A,et al.A new algorithm for bilinear spectral unmixing of hyperspectral images using particle swarm optimization[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(12):5776-5790
    [19] Keshava N,Mustard J F.Spectral unmixing[J].IEEE Signal Processing Magazine,2002,19(1):44-57
    [20] Boardman J W,Kruse F A,Green R O.Mapping target signatures via partial unmixing of AVIRIS data[C]//Summaries of the Fifth Annual JPL Airborne Earth Science Workshop,1995:3-6
    [21] Winter M E.N-FINDR:An algorithm for fast autonomous spectral end-member determination in hyperspectral data[J].Proceedings of SPIE,1999,3753:266-275
    [22] Nascimento J M P,Dias J M B.Vertex component analysis:A fast algorithm to unmix hyperspectral data[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):898-910
    [23] Chang C I,Wu C C,Liu W,et al.A new growing method for simplex-based endmember extraction algorithm[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(10):2804-2819
    [24] Gruninger J H,Ratkowski A J,Hoke M L.The sequential maximum angle convex cone (SMACC) endmember model[J].Proceedings of SPIE,2004,5425:1-14
    [25] Chan T H,Ma W K,Ambikapathi A M,et al.A simplex volume maximization framework for hyperspectral endmember extraction[J].IEEE Transactions on Geoscience and Remote Sensing,2011,椴渹木?㈱〩?名???????????????そ㈠?扩爠?嬬??嵯?兣楡慳渭?奩?味??椠慍?卍?婮桩潭畵???敯瑬?慭汥??祩灭数牬獥灸攠捡瑮牡慬汹?畩湳洺楁砠楦湡杳?瘠楡慬??????獭瀠慴牯猠極瑮祭?捸漠湨獹瑰牥慲楳湰敥摣?湲潡湬渠敤条慴瑡楛癃敝?洯慉瑅牅楅砠?普慴捥瑲潮牡楴穩慯瑮楡潬渠孇?嵯??????呥爠慡湮獤愠捒瑥業潯湴獥?潓湥??敩潮獧挠楓敹湭捰敯?慩湵摭?刲攰洰漸琬攳?卢攭渲猵椰渭杢?有?????????????????㈠???扨物?孃??崬?卵楡湮杧攠牙?前????捡?漮牁搠?呯????愠牡獮?汬慹牳杩敳?獢捡慳汥敤?浭楩确楩湭杵?漭晶?扬牵業来栠瑥?慣湬摯?摩慮牧欠?獩畭牰晬慥捸攠?浬慧瑯敲物楴慨汭猠?慯湲搠?楹浰灥汲楳捰慥瑣楴潲湡獬?晵潮牭?慸湩慮汧祛獊楝献?潅晅?猠灔敲捡瑮牳慡汣?物敯普汳攠捯瑮愠湓捩敧孮?嵬???畯湣慥牳???偧氬愲渰攰琹愬爵礷?匱挱椩攺渴挴攱??漴渴昳攲爼敢湲挾敛????????????????扁爠?嬬??嵡?娠桔愠湈本????椠???呴漠湡杬?元?敡瑮?慥氭?卯瑮畳摴祲?潩普?瑤栠敲?獢灵敳捴琠牭慩汮?浭極硭琭當牯敬?浭潥搠敥汮?潬景?獩潮楧氠?慩湭摰?癥數朠敡瑬慧瑯楲潩湴?業渠?偯潲礠慨湹杰??慳歰敥?慴牲敡慬??桮業湩慸孩?嵧??湝琮敉牅湅慅琠楔潲湡慮汳??潴畩牯湮慳氠?潮映?剥敯浳潣瑩敥?卣敥渠獡楮湤朠??????ㄠ???????????㈱????戱爱?嬺??崹?匭眴愲礰稹攼????氲愹牝欠?剩?乯??爠畄猬救???攠瑒?慅汮??牥潭畢湥摲?瑥牸畴瑲桡楣湴杩??嘠?割?卭?浨楩湧敨牬慹氠?浩慸灥灤椠湤条?慡琠??畩灮牧椠瑭敩?乩敭癵慭搠慶孯?嵵??匠畣浯浮慳牴楲敡獩?潥晤?瑮桯敮?呥桧楡牴摩??渠湭畡慴汲??倠???楴牯扲潩牺湡整??敮潛獊捝椮敉湅捅故?坔潲牡歮獳桡潣灴????㈠???????扣物?孮?づ崠??慤氠楒浥業????汓瑥浮慳湩湮?夬??漰户椬朴攵漨渳?为?收琵?愷氷?丼潢湲氾楛渳攰慝爠?疇源洬榟碮椬渠杵?潉昮?桉礱灥攟狾珏灉攱揣瓷犄懬泋‐椆洆憐枀支獛?畝献楉渱杦?慉?朆斐測攲爰愱氰椬稳攰搨?戩椺氱椶渲攸愭爱?洳漳搠敌汕孏?嵗??????呚版慏湎獇愠捌瑩楡潮湧猬?潈湁??攠潂獩据楧攬湥捴攠?慬渮摉?剤敥浰潥瑮敤?卮整渠獣楯湭杰??づ???????????ㄠ????????扴牲?孬??嵮??捸桩敮獧?佩??畨楹汰汥慲畳浰敥?????戠楲汥業湯整慥爠?扥楮汳楩湮敧愠物?湡潧湥湛敊杝愮瑓楰癥散?浲慯瑳牣楯硰?映慡据瑤漠牓楰穥慣瑴楲潡湬?流敮瑡桬潹摳?晳漬爲‰栱礰瀬攳爰猨瀶攩挺琱父愲永?由渶洳椳砼楢湲朾孛?崱???????敭潥獮捴楯攠湊挠敍?慐測摂?副敵浣潡瑳攭?卩敡湳猠楊渠杍??敹瑰瑥敲牳獰??ぴ???ㄠ?????????????扤爠?孮??嵩?側畵???夠??栠敤湩?婩?坨慬湥杴???敭瑰?慮汥??潳湛獊瑝爮慉楅湅故搠?汲敡慮獳瑡?獴煩畯慮牳攠獯?愠汇来潯牳楣瑩桥浮獣?映潡牮?渠潒湥汭楯湴敥愠牓?畮湳浩楮硧椬渲朰?漲昬‵栰礨瀳攩爺猸瀶攳挭琸爷愸氼?楲派慛朳攲牝礠孍?嵵??????吠牓愬湃獡慲捴瑥楲潥湴猠?漬湂??敥漠獄挬楥整渠捡敬?慂湡摹?剳敩浡潮琠敡?卡敬湹獳楩湳朠??????????????????????扴牡?孵??嵮??桍敡湲???删楣捨桡慩牮搠????潥渠敃楡湲敬?倠?乥潴湨汯楤湳敛慊牝?畃湨浥業硯業湥杴?潩晣?栠祡灮敤爠獉灮整捥瑬牬慩汧?摮慴琠慌?扢慯獲敡摴?潲湹?慓?汳楴湥敭慳爬?洰椰砶琬甸爱攨?温漺渱氳椷渭攱愴爸?晢汲甾捛琳申慝琠楚潡湲?洠潁搬敇污孤?嵲??????呲牳慩湴獹愠捰瑲楯潭湯獴?潮湧?卩楴来湲慡汴?偤爠潣捯敮獳獴楲湡杩??つㄠ??????????つ?????扩牯?嬠??崠??数楥湲穳??????桬愠湩杭??????畊汝氮祉?捅潅渠獇瑥牯慳楣湩敥摮?汥攠慡獮瑤?獒煥畭慯牴敥猠?汥楮湳敩慮牧?獌灥整捴瑥牲慳氬′洰椰砷琬甴爨攳?愺渴愴氶礭猴椵猰?浢敲琾桛漳搴?映潂物?浵慣瑡敳爭楄慩污?焠畊愠湍琬楆晩楧捵慥瑩楲潥湤?椠湍?桁礠灔攮牁獬灴敥捲瑮牡慴汩?楧洠慤杩敲牥祣孴?嵯??????呲物慴湨獭慳挠瑦楯潲渠獣?潮湳??敡潩獮捥楤攠湳捰敡?慳湥搠?剥敧浲潥瑳敳?卯敮渺獁楰湰杬??ちぴ???????????????ctral unmixing[C]//IEEE Workshop on Hyperspectral Image & Signal Processing:Evolution in Remote Sensing,2012:1-4
    [35] Iordache M,Bioucas-Dias J E M,Plaza A.Total variation spatial regularization for sparse hyperspectral unmixing[J].IEEE Transactions on Geoscience and Remote Sens
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

高连如,孙旭,罗文斐,唐茂峰,张兵.高光谱遥感图像混合像元分解的群智能算法[J].南京信息工程大学学报(自然科学版),2018,10(1):81-91
GAO Lianru, SUN Xu, LUO Wenfei, TANG Maofeng, ZHANG Bing. Swarm intelligence algorithms for spectral unmixing in hyperspectral image[J]. Journal of Nanjing University of Information Science & Technology, 2018,10(1):81-91

复制
分享
文章指标
  • 点击次数:1090
  • 下载次数: 2422
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2017-11-10
  • 在线发布日期: 2018-01-25

地址：江苏省南京市宁六路219号    邮编：210044

联系电话：025-58731025    E-mail：nxdxb@nuist.edu.cn

南京信息工程大学学报 ® 2025 版权所有  技术支持：北京勤云科技发展有限公司