A Unified Framework of Multiple Kernels Learning for Hyperspectral Remote Sensing Big Data

Analysis on hyperspectral remote sensing big data is widely in remotely sensed satellite imaging and aerial reconnaissance, the development of sensor technology brought the developing of collecting image data using hyperspectral instruments with hundreds of contiguous spectral channels. Machine learning based hyperspectral sensing data analysis is a feasible way, and among these machine learning methods, kernel learning is a feasible nonlinear feature extraction on hyperspectral sensing data. This paper is to solve the problem of the nonlinear kernel function selection, to improve the system performances of recognition and prediction accuracy. A framework of multiple kernel learning is proposed for classification on hyperspectral remote sensing big data, and some experiments are implemented on two hyperspectral image databases. The comprehensive experiments show that the proposed algorithm is effective on hyperspectral remote sensing big data.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  Alexander Zien,et al.  lp-Norm Multiple Kernel Learning , 2011, J. Mach. Learn. Res..

[3]  M. Omair Ahmad,et al.  Optimizing the kernel in the empirical feature space , 2005, IEEE Transactions on Neural Networks.

[4]  Qi Zhu.  Reformative nonlinear feature extraction using kernel MSE , 2010, Neurocomputing.

[5]  Konstantinos N. Plataniotis,et al.  Face recognition using kernel direct discriminant analysis algorithms , 2003, IEEE Trans. Neural Networks.

[6]  Yung C. Shin,et al.  Sparse Multiple Kernel Learning for Signal Processing Applications , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Gustavo Camps-Valls,et al.  Learning Relevant Image Features With Multiple-Kernel Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[8]  S. Teipel,et al.  Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM , 2015, Human brain mapping.

[9]  Jian Huang,et al.  Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[11]  Sun,et al.  Refined Kernel Principal Component Analysis Based Feature Extraction , 2011 .

[12]  Chen Chen,et al.  Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine , 2014, Remote. Sens..

[13]  Jeng-Shyang Pan,et al.  Kernel class-wise locality preserving projection , 2008, Inf. Sci..

[14]  Hanqing Lu,et al.  Supervised kernel locality preserving projections for face recognition , 2005, Neurocomputing.

[15]  Pengfei Shi,et al.  Uncorrelated discriminant vectors using a kernel method , 2005, Pattern Recognit..

[16]  Lei Wang,et al.  Multiple kernel extreme learning machine , 2015, Neurocomputing.

[17]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[19]  Jeng-Shyang Pan,et al.  Kernel optimization-based discriminant analysis for face recognition , 2009, Neural Computing and Applications.

[20]  Shiguang Shan,et al.  Semisupervised Hashing via Kernel Hyperplane Learning for Scalable Image Search , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  David Zhang,et al.  An alternative formulation of kernel LPP with application to image recognition , 2006, Neurocomputing.

[22]  Ming-Hsuan Yang,et al.  Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[23]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[24]  Lei Wang,et al.  A criterion for optimizing kernel parameters in KBDA for image retrieval , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[25]  Chengjun Liu,et al.  Gabor-based kernel PCA with fractional power polynomial models for face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Zhongliang Jing,et al.  Local structure based supervised feature extraction , 2006, Pattern Recognit..