Spectral and Spatial Kernel Extreme Learning Machine for Hyperspectral Image Classification

Kernel extreme learning machine (ELM) has attracted more and more attentions due to its good performance compared with support vector machine (SVM). Since the original Kernel ELM (KELM) is just a spectral classifier, it can’t extract the rich spatial information of hyperspectral images (HSIs). This hence refrains the performance of KELM. In view of this, based on the fact that the neighbors of a pixel are more likely to belong to the same class, this paper proposes a spectral and spatial KELM, which exploits the local spatial information to improve the KELM for HSIs classification. Experimental results on two well-known datasets demonstrate the good performance of the proposed spectral and spatial KELM compared with the original KELM and other state-of-the-art methods.

[1]  Shutao Li,et al.  Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Zhijing Yang,et al.  Sparse Representation-Based Augmented Multinomial Logistic Extreme Learning Machine With Weighted Composite Features for Spectral–Spatial Classification of Hyperspectral Images , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Stephen Marshall,et al.  Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner] , 2014, IEEE Signal Processing Magazine.

[5]  Yicong Zhou,et al.  Extreme Learning Machine With Composite Kernels for Hyperspectral Image Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Jon Atli Benediktsson,et al.  Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis , 2011, IEEE Geoscience and Remote Sensing Letters.

[8]  Xiaodong Li,et al.  A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm , 2018, IEEE Transactions on Cybernetics.

[9]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Zhe Liu,et al.  Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging. , 2014, Applied optics.

[11]  Antonio J. Plaza,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spectral–Spatial Hyperspectral Image Segmentation Using S , 2022 .

[12]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[13]  Zhijing Yang,et al.  Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification , 2017, Remote. Sens..

[14]  Jon Atli Benediktsson,et al.  Morphological Attribute Profiles for the Analysis of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Ping Ma,et al.  A stability constrained adaptive alpha for gravitational search algorithm , 2018, Knowl. Based Syst..

[16]  K. S. Banerjee.  Generalized Inverse of Matrices and Its Applications , 1973 .

[17]  Junwei Han,et al.  Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing , 2014 .

[18]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[19]  Jon Atli Benediktsson,et al.  Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields , 2016, Remote. Sens..

[20]  Peijun Du,et al.  Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging , 2016, Neurocomputing.

[21]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[22]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[23]  Jon Atli Benediktsson,et al.  Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Yicong Zhou,et al.  Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

[26]  Jon Atli Benediktsson,et al.  Effective Denoising and Classification of Hyperspectral Images Using Curvelet Transform and Singular Spectrum Analysis , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Zhijing Yang,et al.  Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images , 2017, Sensors.

[28]  Stephen Marshall,et al.  Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation , 2015, Comput. Electron. Agric..

[29]  Trac D. Tran,et al.  Hyperspectral Image Classification Using Dictionary-Based Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.