[1]YAO Futian,QIAN Yuntao.Gaussian process and its applications in hyperspectral image classification[J].CAAI Transactions on Intelligent Systems,2011,6(5):396-404.
Copy

Gaussian process and its applications in hyperspectral image classification

References:
[1]WIENER N. Extrapolation, interpolation, and smoothing of stationary time series, with engineering applications[M]. Cambridge, USA: MIT Press, 1949: 102106.
[2]MATHERON G. The intrinsic random functions and their applications[J]. Advances in Applied Probability, 1973, 5(3): 439468.
[3]JOURNEL A G, HUIJBREGTS C J. Mining geostatistics[M]. New York, USA: SpringerVerlag, 1978: 304310.
[4]THOMPSON P D. Optimum smoothing of twodimensional fields[J]. Tellus, 1956, 8(3): 384393.
[5]DALEY R. Atmospheric data analysis[M]. Cambridge, UK: Cambridge University Press, 1993: 99107.
[6]WHITTLE P. Prediction and regulation by linear leastsquare methods[M]. London, UK: English Universities Press, 1984: 5869.
[7]RIPLEY B D. Spatial statistics[M]. Hoboken, USA: WileyIEEE, 2004: 4450.
[8]CRESSIE N. Statistics for spatial data[J]. Terra Nova, 1992, 4(5): 613617.
[9]O’HAGAN A, KINGMAN J F C. Curve fitting and optimal design for prediction[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1978, 40(1): 142.
[10]SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments[J]. Statistical Science, 1989, 4(4): 409423.
[11]SANTNER T J, WILLIAMS B J, NOTZ W. The design and analysis of computer experiments[M]. New York, USA: SpringerVerlag, 2003: 61 65.
[12]WILLIAMS C K I, RASMUSSEN C E. Gaussian processes for regression[M]. Cambridge, USA: MIT Press, 1996: 2537.
[13]RASMUSSEN C E, WILLIAMS C K. Gaussian processes for machine learning[M]. Cambridge, USA: MIT Press, 2006: 1530.
[14]WILLIAMS C K I, BARBER D. Bayesian classification with Gaussian processes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(12): 13421351.
[15]GIBBS M N, MACKAY D J C. Variational Gaussian process classifiers[J]. IEEE Transactions on Neural Networks, 2000, 11(6): 14581464.
[16]NEAL R. Regression and classification using Gaussian process priors[J]. Bayesian Statistics, 1998, 6(10): 475501.
[17]CHANG C I. Hyperspectral imaging: techniques for spectral detection and classification[M]. New York, USA: Kluwer Academic Plenum Publishers, 2003: 816.
[18]MINKA T P. A family of algorithms for approximate Bayesian inference[D]. Cambridge: Massachusetts Institute of Technology, 2001: 3648.
[19]GIBBS M N, MACKAY D J C. Variational Gaussian process classifiers[J]. IEEE Transactions on Neural Networks, 2002, 11(6): 14581464.
[20]SEEGER M. Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers [M]//SOLLA S A, LEEN T K, MULLER K L. Advances in Neural Information Processing Systems. Cambridge, USA: the MIT Press, 2000: 603609.
[21]SHAWETAYLOR J, CRISTIANINI N. Kernel methods for pattern analysis[M]. Cambridge, UK: Cambridge University Press, 2004: 4857.
[22]FLETCHER R. Practical methods of optimization: constrained optimization[M]. Hoboken, USA: John Wiley & Sons Inc, 1984: 8794.
[23]NOCEDAL J, WRIGHT S J. Numerical optimization[M]. New York, USA: SpringerVerlag, 1999: 5364.
[24]URTASUN R, DARRELL T. Discriminative Gaussian process latent variable model for classification[C]//International Conference on Machine Learning. Corvallis, USA, 2007: 934937.
[25]BAUDAT G, ANOUAR F. Generalized discriminant analysis using a kernel approach[J]. Neural Computation, 2000, 12(10): 23852404.
[26]SUGIYAMA M. Local Fisher discriminant analysis for supervised dimensionality reduction[C]//International Conference on Machine Learning. Pittsburgh, USA, 2006: 905912.
[27]GROCHOW K, MARTIN S L, HERTZMANN A, et al. Stylebased inverse kinematics[J]. ACM Transactions on Graphics, 2004, 23(3): 522531.
[28]CSAT L. Gaussian processes: iterative sparse approximation[D]. Birmingham, UK: Aston University, 2005: 2634.
[29]ZHU Xiaojin, GHAHRAMANI Z, LAFFERTY J. Semisupervised learning using Gaussian fields and harmonic functions[C]//Proceedings of the 20th International Conference on Machine Learning. Washington, DC, USA, 2003: 912914.
[30]SINDHWANI V, CHU W, KEERTHI S S. Semisupervised Gaussian process classifiers[C]//International Joint Conference on Artificial Intelligence. Hyderabad, India, 2007: 10591064.
[31]HUGHES G. On the mean accuracy of statistical pattern recognizers[J]. IEEE Transactions on Information Theory, 1968, 14(1): 5563.
[32]QIAN Y, YAO F, JIA S. Band selection for hyperspectral imagery using affinity propagation[J]. IET Computer Vision, 2010, 3(4): 213222.
[33]贺霖,潘泉,邸韦华,等.高光谱图像高维多尺度自回归有监督检测[J]. 自动化学报, 2009, 35(5): 509518. HE Lin, PAN Quan, DI Weihua, et al. Supervised detection for hyperspectral imagery based on high dimensional multiscale autoregression[J]. Acta Automatica Sinica, 2009, 35(5): 509518.
[34]熊桢,童庆禧.用于高光谱遥感图象分类的一种高阶神经网络算法[J]. 中国图象图形学报, 2000, 5(3): 196201.
XIONG Zhen, TONG Qingxi. Highrank artificial neural network algorithm for classification of hyperspectral image data[J]. Journal of Image and Graphics, 2000, 5(3): 196201.
[35]MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 17781790.
[36]刘春红.超光谱遥感图像降维及分类方法研究[D].哈尔滨:哈尔滨工程大学, 2005: 8694.
LIU Chunhong. Research on dimensional reduction and classification of hyperspectral remote sensing image[D]. Harbin: Harbin Engineering University, 2005: 8694.
[37]KITTLER J, PAIRMAN D. Contextual pattern recognition applied to cloud detection and identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 23(6): 855863.
[38]姚伏天,钱沄涛.用于高光谱遥感图像分类的空间约束高斯过程方法[J].南京大学学报:自然科学版, 2009, 45(5): 665670.
?YAO Futian, QIAN Yuntao. A spatial Gaussian process method for hyperspectral remote sensing imagery classification[J]. Journal of Nanjing University: Natural Sciences, 2009, 45(5): 665670.
[39]ROSSI R E, DUNGAN J L, BECK L R. Kriging in the shadows: geostatistical interpolation for remote sensing[J]. Remote Sensing of Environment, 1994, 49(1): 3240.
[40]DENG H, CLAUSI D A. Advanced Gaussian MRF rotationinvariant texture features for classification of remote sensing imagery[C]//Computer Society Conference on Computer Vision and Pattern Recognition. Madison, USA, 2003: 685689.
[41]ZHONG Ping, WANG Runsheng. A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(12): 39783988.
[42]LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning. Williamstown, USA, 2001: 282289.
[43]KUMAR S, HEBERT M. Discriminative random fields[J]. International Journal of Computer Vision, 2006, 68(2): 179201.
[44]LI Jiming, HU Zhenfang, QIAN Yuntao. Hyperspectral data classification using margin infused relaxed algorithm[C]//International Conference on Image Processing. Hong Kong, China, 2009: 16691672.
[45]LI Jiming, QIAN Yuntao. Regularized multinomial regression method for hyperspectral data classification via pathwise coordinate optimization[C]//Digital Image Computing: Techniques and Applications. Melbourne, Australia, 2009: 540545.
[46]YAO Futian, Qian Yuntao. Band selection based Gaussian processes for hyperspectral remote sensing images classification[C]//International Conference on Image Processing. Hong Kong, China, 2009: 28452848.
[47]VATSAVAI R R, SHEKHAR S, BURK T E. A semisupervised learning method for remote sensing data mining[C]//International Conference on Tools with Artificial Intelligence. Hong Kong, China, 2005: 205211.
[48]BELKIN M, NIYOGI P, SINDHWANI V. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples[J]. The Journal of Machine Learning Research, 2006, 7: 23992434.
Similar References:

Memo

-

Last Update: 2011-11-16

Copyright © CAAI Transactions on Intelligent Systems