Computer Science ›› 2018, Vol. 45 ›› Issue (12): 251-254,278.doi: 10.11896/j.issn.1002-137X.2018.12.041

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Hyperspectral Unmixing Algorithm Based on Dual Graph-regularized Semi-supervised NMF

ZOU Li1, CAI Xi-biao1, SUN Jing2, SUN Fu-ming1   

  1. (School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou,Liaoning 121001,China)1
    (School of Software Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China)2
  • Received:2017-12-20 Online:2018-12-15 Published:2019-02-25

Abstract: In hyperspectral images,the existence of mixed pixels greatly impedes the development of hyperspectral remote sensing technology.Therefore,how to carry out unmixing accurately and efficiently in the process of using spectral images is a key problem.For hyperspectral unmixing,using original non-negative matrix factorization (NMF) algorithm faces some difficulties,for example,the objective function is non-convex function,so it is difficult to solve the global optimal solution.Besides,the pure pixel like element doesn’t exist in mixed pixel.In order to solve these problems,this paper proposed a mixed pixel unmixing algorithm namely dual graph-regularized constrained semi-supervised NMF (DCNMF) .This algorithm adopts gradient descent algorithm and iterative updating rule,considers the geometric structures of hyperspectral data manifold and the spectral feature manifold,and can jump out of the local extremum,thus solving the global optimal solution.Real hyperspectral image data simulation experiments show that DCNMF algorithm can be used to decompose the mixed pixel accurately and efficiently,enhancing the effect of unmixing,improving the accuracy of mixing,saving the computing time and speeding up convergence.

Key words: Hyperspectral images, Mixed pixel disintegration, Nonnegative matrix factorization, Bigraph regularization

CLC Number: 

  • TP391
[1]GOETZ A F,VANE G,SOLOMON J E,et al.Imaging spec-trometry for earth remote sensing[J].Science,1985,228(4704):1147-1153.
[2]VANE G,GREEN R,CHRIEN T G,et al.The airborne visible/infrared imaging spectrometer(AVIRIS)[J].Remote Sensing of Environment,1993,44(2):127-143.
[3]GREEN R,EASTWOOD M L,SARTURE C M,et al.Imaging spectroscopy and the airborne visible/infrared imaging spectrometer(AVIRIS)[J].Remote Sensing of Environment,1998,65(3):227-248.
[4]LEE D D,SEUNG H S.Learning the parts of objects by non-ne-gative matrix factorization[J].Nature,1999,401(6755):788-791.
[5]TONG L,ZHOU J,QIAN Y T.Nonnegative Matrix Factorization Based Hyperspectral Unmixing with Partially Known Endmembers[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(11):6531-6544.
[6]YU Y,GUO S,SUN W D.Minimum distance constrained nonnegative matrix factorization for the endmember extraction of hyperspectral images[C]∥Proceeding of Remote Sensing and GIS Data Processing and Applications.Wuhan,2007:6790151-6790159.
[7]JIA S,QIAN Y T,JI X,et al.Hyperspectral Unmixing Algorithm Based on spectral and spatial characteristics[J].Journal of Shenzhen University(Science & Engineering),2009,26(3):162-167.(in Chinese)
贾森,钱沄涛,纪霞,等.基于光谱和空间特性的高光谱解混方法[J].深圳大学学报(理工版),2009,26(3):162-167.
[8]YANG S Y,ZHANG X T,YAO Y G,et al.Geometric Nonnegative Matrix Factorization (GNMF) for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sening,2015,8(6):2696-2703.
[9]WANG W H,QIAN Y T,TANG Y Y.Hypergraph-Regularized Sparse NMF for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sening,2016,9(2):681-694.
[10]YUAN Y,FU M,LU X Q.Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(6):2975-2986.
[11]ADAMS J B,SABOL D E,KAPOS V,et al.Classification of multispectral images based on fractions of endmembers:Application to land-cover change in the Brazilian Amazon[J].Remote Sensing of Environment,1995,52(2):137-154.
[12]ADAMS J B,SMITH M O,JOHNSON P E.Spectral mixture modeling:of rock and soil types at the Viking Larder 1 site[J].Journal of Geophysical Research:Solid Earth(1978-2012),1986,91(8):8098-8112.
[13]DIAS J B,PLAZA A.Hyperspectral unmixing geometrical,statistical and sparse regression-based approaches[C]∥Procee-dings of SPIE:Image and Signal Processing for Remote Sensing XVI.Toulouse,France:SPIE Press,2010.
[14]ZHAO C H,CHENG B Z,YANG W C.A hyperspectral unmi-xing algorithm based on the constraint nonnegative matrix decomposition[J].Journal of Harbin Institute of Technology,2012,33(3):378-382.(in Chinese)
赵春晖,成宝芝,杨伟超.利用约束非负矩阵分解的高光谱解混算法.哈尔滨工业大学学报,2012,33(3):378-382.
[15]SONG Y G,WU Z B,WEI Z H,et al.Survey of sparsity constrained hyperspectral unmixing.Journal of Nanjing University of Science and Technoloogy,2013,37(4):486-492.(in Chinese)
宋义刚,吴泽彬,韦志辉,等.稀疏性高光谱解混方法研究[J].南京理工大学学报,2013,37(4):486-492.
[16]KONG F J,BIAN C D,LI Y S,et al.Hyperspectral unmixing method for non-convex and low rank constraints[J].Journal of Xi’an Electronic and Science University(Natural Science Edition),2016,43(6):116-121.(in Chinese)
孔繁锵,卞陈鼎,李云松,等.非凸稀疏低秩约束的高光谱解混方法.西安电子科技大学学报(自然科学版),2016,43(6):116-121.
[17]WANG T C,LIU X Z,DONG Z Z,et al.An adaptive robust minimum volume hyperspectral unmixing algorithm[J].Journal of Automation,2017,43(2):1-19.(in Chinese)
王天成,刘相振,董泽政,等.一种自适应鲁棒最小体积高光谱解混算法[J].自动化学报,2017,43(2):1-19.
[18]LIU H,WU Z,CAI D,et al.Constrained non-negative matrix factorization for image representation[J].IEEE Transactions Pattern Analysis and Machine Intelligence,2012,34(7):1299-1311.
[19]SHU Z Q,ZHAO C X.Constrained nonnegative matrix decomposition algorithm based on graph regularization and its application in image representation[J].Pattern Recognition and Artificial Intelligence,2013,26(3):300-306.(in Chinese)
舒振球,赵春霞.基于图正则化的受限非负矩阵分解算法及其在图像表示中的应用[J].模式识别与人工智能,2013,26(3):300-306.
[20]PLAZA A,MARTINEZ P,PEREZ R,et al.A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data[J].IEEE Geoscience and Remote Sensing Letters,2004,42(3):650-663.
[21]KESHAVA N,MUSTARD J F.Spectral unmixing[J].IEEESignal Process Mag,2002,19(1):44-57.
[22]LANDGREBE D.Multispectral data analysis:a signal theoryperspective[D].West Lafayette:Purdye University,1998.
[23]SWAYZE G.The hydrothermal l and structural history of the cuprite mining district,southwestern Nevada:an integrated geological and geophysical approach[D].Boulde:University of Co-lorado,1997.
[1] HE Xiao-wen, HU Yi-fei, WANG Hai-ping, CHEN Mo. Online Learning Nonnegative Matrix Factorization [J]. Computer Science, 2019, 46(6A): 473-477.
[2] HUANG Meng-ting, ZHANG Ling, JIANG Wen-chao. Multi-type Relational Data Co-clustering Approach Based on Manifold Regularization [J]. Computer Science, 2019, 46(6): 64-68.
[3] JIA Xu, SUN Fu-ming, LI Hao-jie, CAO Yu-dong. Vein Recognition Algorithm Based on Supervised NMF with Two Regularization Terms [J]. Computer Science, 2018, 45(8): 283-287.
[4] YU Xiao, NIE Xiu-shan, MA Lin-yuan and YIN Yi-long. Robust Video Hashing Algorithm Based on Short-term Spatial Variations [J]. Computer Science, 2018, 45(2): 84-89.
[5] REN Shou-gang, WAN Sheng, GU Xing-jian, WANG Hao-yun, YUAN Pei-sen, XU Huan-liang. Hyperspectral Image Classification Based on Multi-scale Discriminative Spatial-spectral Features [J]. Computer Science, 2018, 45(12): 243-250.
[6] SUN Jing, CAI Xi-biao, JIANG Xiao-yan and SUN Fu-ming. Graph Regularized and Incremental Nonnegative Matrix Factorization with Sparseness Constraints [J]. Computer Science, 2017, 44(6): 298-305.
[7] TANG Bing, Laurent BOBELIN and HE Hai-wu. Parallel Algorithm of Nonnegative Matrix Factorization Based on Hybrid MPI and OpenMP Programming Model [J]. Computer Science, 2017, 44(3): 51-54.
[8] JIANG Xiao-yan, SUN Fu-ming and LI Hao-jie. Semi-supervised Nonnegative Matrix Factorization Based on Graph Regularization and Sparseness Constraints [J]. Computer Science, 2016, 43(7): 77-82, 105.
[9] LIANG Qiu-xia, HE Guang-hui, CHEN Ru-li and CHU Jian-pu. Research of Face Recognition Algorithm Based on Nonnegative Tensor Factorization [J]. Computer Science, 2016, 43(10): 312-316.
[10] HU Xue-kao, SUN Fu-ming and LI Hao-jie. Constrained Nonnegative Matrix Factorization with Sparseness for Image Representation [J]. Computer Science, 2015, 42(7): 280-284, 304.
[11] LI Qian,JING Li-ping and YU Jian. Multi-kernel Projective Nonnegative Matrix Factorization Algorithm [J]. Computer Science, 2014, 41(2): 64-67.
[12] PAN Yin-song,WANG Pan-feng,HUANG Hong and LIU Yan. Hyperspectral Remote Sensing Image Classification Based on SSLPP [J]. Computer Science, 2013, 40(Z11): 333-336,373.
[13] . Nonnegative Matrix Factorization-based IP Traffic Prediction [J]. Computer Science, 2012, 39(1): 48-52.
[14] DU Bo,ZHANG Liang-pei,LI Ping-xiang,ZHONG Yan-fei,CHEN Tao. Anomaly Detection Method Based on Random Field for Hyperspectral Imagery [J]. Computer Science, 2010, 37(6): 289-292.
[15] JIANG Wei, YANG Bing-ru,SUI Hai-feng. Local Sensitive Nonnegative Matrix Factorization [J]. Computer Science, 2010, 37(12): 211-214.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[3] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[4] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[5] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[6] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[7] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[8] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[9] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[10] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .