Please wait a minute...
img

官方微信

遥感技术与应用  2015, Vol. 30 Issue (6): 1195-1205    DOI: 10.11873/j.issn.1004-0323.2015.6.1195
图像与数据处理     
高光谱影像端元提取算法的进展分析与比较
苏远超1,孙旭2,高连如2,陈晓宁1
(1.西安科技大学测绘科学与技术学院,陕西 西安710054;
2.中国科学院遥感与数字地球研究所,北京100094)
The Analysis and Comparison of Hyperspectral Endmember Extraction Algorithms
Su Yuanchao1,Sun Xu2,Gao Lianru2,Chen Xiaoning1
(1.College of Geomatics,Xi’an University of Science and Technology,Xi’an 710054,China;
2.Institution of Remote Sensing and Digital Earth Chinese Academy of Sciences,Beijing 100094,China)
 全文: PDF(9897 KB)  
摘要:

对当前国际经典和前沿的6种代表性的端元提取算法进行比较研究,包括SPP\|N\|FINDR、VCA、SPICE、PCOMMEND、MVSA和MVC\|NMF,通过理论和实验两种方式对这些算法进行综合性对比和分析,总结其优势和存在的问题。通过模拟和真实数据实验得出:SPP-N-FINDR算法的抗噪声能力不如其他5种算法;VCA和MVSA的稳定性较好;MVC-NMF和SPICE无需知道端元数目,且能直接得出丰度矩阵,自动化程度较高;PCOMMEND在真实高光谱图像中提取端元的结果最好,能直接得出丰度矩阵,但若端元数量为素数时精度会下降。研究成果将为今后围绕这些算法的相关研究提供必要的理论支持和参考。

关键词: 高光谱混合像元分解端元提取    
Abstract:

This paper summarizes six popular and cutting\|edge algorithms,including SPP\|N\|FINDR,VCA ,SPICE,PCOMMEND,MVSA and MVC\|NMF.A comprehensive comparison and analysis concludes the advantages and disadvantages of each of the six algorithms.From the experimental results show that this paper concludes that SPP\|N\|FINDR algorithm lacks the ability to resist noise when compared to the other five algorithms;VCA and MVSA are more stable than the other five algorithms; MVC\|NMF and SPICE can autonomously determine the number of endmembers and simultaneously can also obtain abundance matrix; The outcomes of PCOMMEND by true hyperspectral image which is best and gain abundance matrix,but the accuracy of this algorithm declines when the number of endmembers is prime.In the future,embracing these algorithms to process relational study,the research will offer theoretical support and consult.

Key words: Hyperspectral    Hyperspectral unmixing    Endmember extraction
收稿日期: 2014-11-08 出版日期: 2016-01-25
:  TP 751  
基金资助:

国家自然科学基金青年基金项目(41201356)。

通讯作者: 高连如(1979-),男,北京人,博士,副研究员,主要从事高光谱图像信息提取方面的研究。Email:gaolr@radi.ac.cn。    
作者简介: 苏远超(1988-),男,陕西西安人,硕士研究生,主要从事高光谱图像混合像元分解方面的研究。Email:knightsuyuanchao@126.com。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
苏远超
孙旭
高连如
陈晓宁

引用本文:

苏远超,孙旭,高连如,陈晓宁. 高光谱影像端元提取算法的进展分析与比较[J]. 遥感技术与应用, 2015, 30(6): 1195-1205.

Su Yuanchao,Sun Xu,Gao Lianru,Chen Xiaoning. The Analysis and Comparison of Hyperspectral Endmember Extraction Algorithms. Remote Sensing Technology and Application, 2015, 30(6): 1195-1205.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.6.1195        http://www.rsta.ac.cn/CN/Y2015/V30/I6/1195

[1]Tong Qingxi,Zhang Bing,Zheng Lanfen.Hyperspectral Remote〖HJ1.91mm〗 Sensing[M].Beijing:High Education Press,2007.[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,2007.]

[2]Winter E M.N-FINDR:An Algorithm for Fast Autonomous Spectral Endmember Determination in Hyperspectral Data[C]//SPIE Conference on Image Spectrometry,1999:266-275.

[3]Nascimento J M P,Bioucas-Dias J M.Vertex Component Analysis:A Fast Algorithm to Unmix Hyperspectral Data[J].IEEE Transactions on Geoscence and Remote Sensing,2005,43(4):898-910.

[4]Zare A,Gader P.Sparsity Promoting Iterated Constrained Endmember Detection in Hyperspectral Imagery[J].IEEE Geoscience and Remote Sensing Letters,2007,4(3):446-450.

[5]Zare A,Gader P.Piece-wise Convex Multiple-model Endmember Detection and Spectral Unmixing[J].IEEE Transations on Geoscience and Remote Sensing,2013:51(5):2853-2861.

[6]Li J,Bioucas-Dias J M.Minimum Volume Simplex Analysis:A Fast Algorithm to Unmix Hyperspectral Data[C]//IEEE International Geoscience and Remote Sensing,2008:250-253.

[7]Miao L,Qi H.Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained[J].IEEE Transactions on Geoscence and Remote Sensing,2007,45(3):765-777.

[8]Zortea M,Plaza A.Spatial Preprocessing for Endmember Extraction[J].IEEE International Geoscience and Remote Sensing,2009,47(8):2679-2693.

[9]Berman M,Kiiveri H,Lagerstrom R,et al.ICE:A Statistical Approach to Identifying Endmembers in Hyperspectral Images[J].IEEE Transations on Geoscience and Remote Sensing,2004,42(10):2085-2095.

[10]Zare A,Gader P.PCE:Piece-wise Convex Endmember Detection and Spectrual Unmixing[J].IEEE Transations on Geoscience and Remote Sensing,2013,51(5):1-14.

[11]Maurice D C,Minimum-volume Transforms for Remotely Sensed Data[J].IEEE Transactions on Geoscience and Remote Sensing,1994,32(3):542-552.

[12]Abrams M J,Ashley R P,Rowan L C,et al.Mapping of Hydrothermal Alteration in the Cuprite Mining District,Nevada,Using Aircraft Scanner Image for the Spectral Region 0.46 to 2.36 Symbolm[J].Geology,1977,5(12):713-718.

[13]Resmini R,Kappus M,Aldrich W,et al.Mineral Mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) Sensor Data at Cuprite Nevada,USA[J].International Journal of Remote Sensing,1997,18(7):1553-1570.

[14]Lee D D,Seung H S.Algorithms and Applications for Approximate Nonnegative Matrix Factorization[J].Elsevier Computation Statistics& Data Analysis,2007,52(2007):155-173.[15]Zhang X,Tong X,Liu M.An Improved N-FINDR Algorithm for Endmember Extraction in Hyperspectral Imagery[C]//IEEE Urban Remote Sensing Joint.Event,2009:978-983.

[16]Bioucas-Dias J M,Nascimento J M.Hyperspectral Subspace Identification[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(8):2435-2445

[17]Bioucas-Dias J M,Plaze A,Dobigon N,et al.Hyperspectral Unmixing Overview:Geometrical,Statistical,and Sparse Regression-based Approaches[J].IEEE Journal of Selected Topics in Earth Obeservation and Remote Sensing,2012,5(2):354-379.

[18]Charles E A.Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Prombles[J].JSTOR The Annual of Statistics,1974,2(6):1152-1174.

[19]Geng X,Zhao Y,Wang F,et al.A New Volume Formula for A Simplex and Its Application to Endmember Extraction for Hyperspectral Image Analysis[C]//IEEE International Journal of Remote Sensing,2010:1027-1035.

[20]Victor F H,Yosio E S.Spectral Linear Mixing Model in Low Spatial Resolution Image Data[J].IEEE Transations on Geoscience and Remote Sensing,2005,43(11):2555-2562.[21]Lopez S,Horstrand P,Callico G M,et al.A Low-Computational-Complexity Algorithm for Hyperspectral Endmember Extraction:Modified Vertex Component Analysis[J] IEEE Geoscience and Remote Sensing  Letter,2012,9(3):502-506.

[22]Plaza A,Martinez P,Plaza Javier,et al.A Quantitative and Comparative Analysis of Endmember Extraction Algorithms from Hyperspectral Data[J].IEEE Transactions on Geoscence and Remote Sensing,2004,42(3):650-663.

[23]Zare A,Gader P.George Casella.Sampling Piecewise Convex Unmixing and Endmember Extraction[J].IEEE Transactions on Geoscence and Remote Sensing,2013,51(3):1655-1665.

[24]Gao Jianwei.Research on Hyperspectral Images Information Extraction Algorithms based on Ant Colony Optimization[D].Beijing:University of Chinese Academy of Sciences,2005.[ 高建威.基于蚁群优化算法的高光谱图像信息提取方法研究[D].北京:中国科学院大学,2014.]

[25]Xue Qi,Kuang Gangyao,Li Zhiyong.Endmember Extraction Algorithms from Hyperspectral Image based on The Linear Mixing Model:An Overview[J].Remote Sensing Technology and Application,2004,19(31):197-201.[ 薛绮,匡纲要,李智勇.基于线性混合模型的高光谱图像端元提取[J].遥感技术与应用,2004,19(31):197-201.]

[26]Peng Qingqing,Yang Liao,Wang Jie,et al.Endmember Extraction based on Anomaly Detection[J].Remote Sensing Technology and Application,2011,26(4):457-461.[ 彭青青,杨辽,王杰,等.基于异常探测的高光谱端元提取方法研究[J].遥感技术与应用,2011,26(4):457-461.]

[27]SunXu.Research on Endmember Extraction Algorithm of Hyperspectral Remote Sensing Images based on Swarm Intelligence[D].Beijing:Center for Earth Observation and Digital Earth Chinese Academy of Sciences,2011.[ 孙旭.基于群智能算法的高光谱遥感图像端元提取方法研究[D].北京:中国科学院对地观测与数字地球科学中心,2011.]


 

[1] 陈伟民,张凌,宋冬梅,王斌,丁亚雄,许明明,崔建勇. 基于AdaBoost改进随机森林的高光谱图像地物分类方法研究[J]. 遥感技术与应用, 2018, 33(4): 612-620.
[2] 苏阳,祁元,王建华,徐菲楠,张金龙. 基于航空高光谱影像的额济纳绿洲土地覆被提取[J]. 遥感技术与应用, 2018, 33(2): 202-211.
[3] 秦振涛,杨茹,张靖,杨武年. 基于聚类结构自适应稀疏表示的高光谱遥感图像修复研究[J]. 遥感技术与应用, 2018, 33(2): 212-215.
[4] 郭宇柏,卓莉,陶海燕,曹晶晶,王芳. 基于空谱初始化的非负矩阵光谱混合像元盲分解[J]. 遥感技术与应用, 2018, 33(2): 216-226.
[5] 刘爱林,郭宝平,李岩山 . 基于离散粒子群算法的凸多模态高光谱图像端元提取研究[J]. 遥感技术与应用, 2018, 33(2): 227-232.
[6] 吴兴,张霞,孙雪剑,张立福,戚文超. SPARK卫星高光谱数据辐射质量评价[J]. 遥感技术与应用, 2018, 33(2): 233-240.
[7] 宋婷婷,付秀丽,陈玉,魏永明,王钦军,程先锋. 云南个旧矿区土壤锌污染遥感反演研究[J]. 遥感技术与应用, 2018, 33(1): 88-95.
[8] 刘慧珺,苏红军,赵-波. 基于改进萤火虫算法的高光谱遥感多特征优化方法[J]. 遥感技术与应用, 2018, 33(1): 110-118.
[9] 王凯,赵军,朱国锋. 基于GF-1遥感数据决策树与混合像元分解模型的冬小麦种植面积早期估算[J]. 遥感技术与应用, 2018, 33(1): 158-167.
[10] 李伟娜,韦玮,张怀清,刘华. 基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J]. 遥感技术与应用, 2017, 32(5): 809-817.
[11] 肖昊,王杰. 基于IDL和MATLAB混合编程的两种光谱混合分析方法比较[J]. 遥感技术与应用, 2017, 32(5): 858-865.
[12] 李颖,陈怀亮,李耀辉. 利用夏玉米端元丰度估算夏玉米种植面积的研究[J]. 遥感技术与应用, 2017, 32(5): 913-920.
[13] 唐超,邵龙义. 高光谱遥感地物目标识别算法及其在岩性特征提取中的应用[J]. 遥感技术与应用, 2017, 32(4): 691-697.
[14] 李恒凯,欧彬,刘雨婷,邱玉宝. 基于混合像元分解的高光谱影像柑橘识别方法[J]. 遥感技术与应用, 2017, 32(4): 743-750.
[15] 苏红军,赵波. 基于共形几何代数的高光谱遥感波段选择方法[J]. 遥感技术与应用, 2017, 32(3): 539-545.