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遥感技术与应用  2015, Vol. 30 Issue (1): 82-91    DOI: 10.11873/j.issn.1004-0323.2015.1.0082
图像与数据处理     
非负矩阵分解融合高光谱和多光谱数据
崔艳荣1,何彬彬1,张瑛1,李蔓2
(1.电子科技大学资源与环境学院,四川 成都611731; 
2.贵州省环境保护厅电子政务中心,贵州 贵阳550000)
Fusion of Hyperspectral and Multispectral Data Using Nonnegative Matrix Factorization
Cui Yanrong1,He Binbin1,Zhang Ying1,Li Man2
(1.University of Electronic Science and Technology of China,School of
Resource and Environment,Chengdu 611731,China;
2.E-government Center of Environmental Protection Office in Guizhou Province,Guiyang 550000,China)
 全文: PDF(3985 KB)  
摘要:

由于光谱分辨率和空间分辨率的制约以及物理条件的限制,高光谱数据具有很高的光谱分辨率而其空间分辨率却很低。因此,一般高光谱数据的空间分辨率往往低于仅有几个波段的多光谱数据的空间分辨率。高光谱数据和多光谱数据的融合可以得到同时具有高空间分辨率和高光谱分辨率的数据,进而应用于更高空间分辨率下地物的识别和分类。非负矩阵分解(Nonnegative Matrix Factorization)算法用于实现低空间分辨率高光谱数据和高空间分辨率多光谱数据的融合。首先利用顶点成分分析法VCA(Vertex Component Analysis)分解高光谱数据,得到初始的端元波谱矩阵和端元丰度矩阵;然后用非负矩阵分解算法交替地对高光谱数据和多光谱数据进行分解,得到高光谱分辨率的端元波谱矩阵和高空间分辨率的丰度矩阵;最后两个矩阵相乘得到高空间分辨率和高光谱分辨率的融合结果。在每一步非负矩阵分解过程中,数据之间的传感器观测模型用于分解矩阵的初始化。AVIRIS和HJ-1A数据实验结果分析表明:非负矩阵分解算法有效提高了高光谱数据的所有波长范围内波段数据的空间分辨率,而高精度的融合结果可用于地物的目标识别和分类。

关键词: 非负矩阵分解高光谱多光谱融合    
Abstract:

The hyperspectral data has very high spectral resolution,but low spatial resolution with the trade\|off between the spectral and spatial resolution,as well as the physical limits.Therefore,in many cases,the spatial resolution of the hyperspectral image system is lower than the multispectral image system that has the less spectral channels.The fusion of the hyperspectral and multispectral data can produce fused data with high spatial and spectral resolution which can contribute to the identification and classification of the material at a finer spatial resolution.NMF (Nonnegative Matrix Factorization) algorithm is used to perform the fusion of low spatial resolution hyperspectral data and high spatial resolution multispectral data.Firstly,VCA (Vertex Component Analysis) is used to perform the factorization of hyperspectral data to obtain endmember spectra matrix and abundance matrix; Secondly,NMF(Nonnegative Matrix Factorization) is used to unmix the hyperspectral and multispectral data alternatively to obtain high spectral resolution endmember spectra matrix W and high spatial resolution abundance matrix H.Finally,fusion data with high spectral resolution and high spatial resolution can be obtained by multiplying the two matrices,.Sensor observation models of the data are built in the initialization matrix of each NMF unmixing procedure.The experiments with AVIRIS data and HJ-1A data have shown that NMF method can be used to improve the spatial resolution on all wavelength regions,and the higher qualities of the estimated data by NMF can be used for the classification of the materials and identification at a finer spatial resolution.

Key words: Nonnegative matrix factorization    Hyperspectral    Multispectral    Fusion
收稿日期: 2014-01-05 出版日期: 2015-03-11
:  TP 75  
基金资助:

国家自然科学基金项目(41171302)“耦合不确定性空间推理和案例推理的区域矿产资源潜力预测模型研究”,中央高校基本科研业务费专项资金(ZYGX2012J155),贵州省重大科技专项(黔科合重大专项字\[2012\]6007)“‘数字环保’关键技术研究及应用示范”资助。

通讯作者: 何彬彬(1972-),男,湖南郡阳人,教授,博导,主要从事数据挖掘和定量遥感方面的研究。Email: binbinhe@uestc.edu.cn。    
作者简介: 崔艳荣(1987-),女,河南濮阳人,硕士研究生,主要从事遥感图像融合方面的研究。Email:cyrmay4@126.com。
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引用本文:

崔艳荣,何彬彬,张瑛,李蔓. 非负矩阵分解融合高光谱和多光谱数据[J]. 遥感技术与应用, 2015, 30(1): 82-91.

Cui Yanrong,He Binbin,Zhang Ying,Li Man. Fusion of Hyperspectral and Multispectral Data Using Nonnegative Matrix Factorization. Remote Sensing Technology and Application, 2015, 30(1): 82-91.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.1.0082        http://www.rsta.ac.cn/CN/Y2015/V30/I1/82

[1]Wald L.Data Fusion:Definitions and Architectures.Fusion of Images of Different Spatial Resolutions[M].Paris,France:Les Presses de l’écoledes Mines,2002.

[2]Shettigara V K.A Generalized Component Substitution Technique for Spatial Enhancement of Multispectral Images Using a Higher Resolution Data Set[J].Photogrammetric Engineering and Remote Sensing,1992,58(5):561-567.

[3]Carper W J,Lillesand T M,Kiefer R W.The Use of Intensity-hue-saturation Transformations for Merging SPOT Panchromatic and Multispectral Image Data[J].Photogrammetric Engineering and Remote Sensing,1990,56(4):459-467.

[4]Gillespie R,Kahle A B,Walker R E.Color Enhancement of Highly Correlated images—II Channel Ratio and ‘Chromacity’ Transformation Techniques[J].Remote Sensing of Environment,1987,22(3):343-365.

[5]Filberti D P,Marsh S E,Schowengardt R A.Synthesis of Imagery from High Spatial and Spectral Resolution from Multiple Imagesources[J].Optical of Engineering,1994,33(8):49-56.

[6]Ranchin T,Wald L.Fusion of High Spatial and Spectral Resolution Images:The ARSIS Concept and Its Implementation[J].Photogrammetric Engineering and Remote Sensing.2000,66(1):4-18.

[7]Pradines D.Improving SPOT Image Size and Multispectral Resolution[C]//Proceeding of the International Society for Optical Engineering Conference:Earth Remote Sensing Using Landsat Thematic Mapper SPOT System,1986,0660:78-102.

[8]Choi M,Kim R Y,Nam M R,et al.Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transform[J].IEEE Geoscience and Remote Sensing Letters,2005,2(2):136-140.

[9]Hao H,Yu S Y,Song L,et al.Fusion of Multispectral and Panchromatic Satellite Images based on Contourlet Transform and Local Average Gradient[J].Optical Engineering,2007,46(2):020502.

[10]Gomez R,Jazaeri A,Kafatos M.Wavelet-based Hyperspectral and Multispectral Image Fusion[C]//Proceeding of the International Society for Optical Engineering,2001,4383:36-42.

[11]Zhang Y F,He M Y.Multi-spectral and Hyperspectral Image Fusion Using 3-D Wavelet Transform[J].Journal of Electronics (China),2007,24(2):218-224.[12]Hardie R C,Eismann M T.MAP Estimation for Hyperspectral Image Resolution Enhancement Using an Auxiliary Sensor[J].IEEE Transactions on Image Processing,2004,13(9):1174-1184.

[13]Stocker D,Schaum A P.Application of Stochastic Mixing Models to Hyperspectral Detection Problems[C]//Proceeding of the International Society for Optical Engineering,1997,3071:47-60,

[14]Eismann M T,Hardie R C.Application of the Stochastic Mixing Model to Hyperspectral Resolution Enhancement[J].IEEE Transactions on Geoscience and Remote Sensensing,2004,42(9):1924-1933.

[15]Eismann M T,Hardie R C.Hyperspectral Resolution Enhancement Using High-resolution Multispectral Imagery with Arbitrary Response Functions[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(3):455-465.

[16]Zhang Y F,Backer S D,Scheunders P.Noise-resistant Wa-veletbased Bayesian Fusion of Multispectral and Hyperspectral Images[J].IEEE Transactions on Geoscience and Remote Sensensing,2009,47(11):3834-3843.

[17]Gross H N,Schott J R.Application of Spectral Mixture Analysis and Image Fusion Techniques for Image Sharpening[J].Remote Sensing of Environment,1998,63(2):85-94.

[18]Robinson G D,Gross H N,Schott J R.Evaluation of Two Applications of Spectral Mixing Models to Image Fusion[J].Remote Sensing of Environment,2000,71(3):272-281.

[19]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 Sensensing,2005,43(4):898-910.

[20]Boardman J W.Geometric Mixture Analysis of Imaging Spectrometry Data[C]//Proceeding of IEEE International Geoscience and Remote Sensing Symposium,Pasadena,CA,1994,4:2369-2371.

[21]Liu X S,Xia W,Wang B,et al.An Approach based on Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(2):757-772.

[22]Yokoya N,Yairi T,Iwasaki A.Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(2):455-465.

[23]Lee D D,Seung H S.Learning the Parts of Objects by Nonnegative Matrix Factorization[J].Nature,1999,401(6755):788-791.

[24]Niu Liming,Meng Jihua,Wu Bingfang,et al.Research on Standard Preprocessing Flow for HJ-1A HSI Level 2 Data Product[J].Remote Sensing for Land & Resources,2011,88(1):77-82.[钮立明,蒙继华,吴炳方,等.HJ-1A星HSI数据2级产品处理流程研究[J].国土资源遥感,2011,88(1):77-82.]


 

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