A Research on the Combination Strategies of Multiple Features for Hyperspectral Remote Sensing Image Classification

It has been common to employ multiple features in the identification of the images acquired by hyperspectral remote sensing sensors, since more features give more information and have complementary properties. Few studies have discussed the combination strategies of multiple feature groups. This study made a systematic research on this problem. We extracted different groups of features from the initial hyperspectral images and tried different combination scenarios. We integrated spectral features with different textural features and employed different dimensionality reduction algorithms. Experimental results on three widely used hyperspectral remote sensing images suggested that “dimensionality reduction before combination” performed better especially when textural features performed well. The study further compared different combination frameworks of multiple feature groups, including direct combination, manifold learning, and multiple kernel method. The experimental results demonstrated the effectiveness of direct combination with an autoweight calculation.

[1]  Jon Atli Benediktsson,et al.  A spatial-spectral kernel-based approach for the classification of remote-sensing images , 2012, Pattern Recognit..

[2]  Marco Diani,et al.  A New Band Selection Strategy for Target Detection in Hyperspectral Images , 2008, KES.

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

[4]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[5]  Peijun Du,et al.  Polarimetric SAR image classification by Boosted Multiple-Kernel Extreme Learning Machines with polarimetric and spatial features , 2014 .

[6]  Yi Wang,et al.  A Spectral-Spatial Classification of Hyperspectral Images Based on the Algebraic Multigrid Method and Hierarchical Segmentation Algorithm , 2016, Remote. Sens..

[7]  Wei Cheng,et al.  Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier , 2016, J. Sensors.

[8]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[9]  Hongyuan Zha,et al.  Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment , 2002, ArXiv.

[10]  Jon Atli Benediktsson,et al.  Multiple Feature Learning for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

[12]  Liangpei Zhang,et al.  On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Reiner Lenz,et al.  Generalized co-occurrence matrix for multispectral texture analysis , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[14]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[15]  Guang Yang,et al.  Improving remote sensing image classification by exploiting adaptive features and hierarchical hybrid decision trees , 2017 .

[16]  Gunnar Rätsch,et al.  Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..

[17]  Yongdong Zhang,et al.  Multiview Spectral Embedding , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[19]  Chunhong Pan,et al.  Automatic Spatial–Spectral Feature Selection for Hyperspectral Image via Discriminative Sparse Multimodal Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Pai-Hui Hsu,et al.  Feature extraction of hyperspectral images using wavelet and matching pursuit , 2007 .

[21]  Chih-Fong Tsai,et al.  Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..

[22]  Juan Yang,et al.  Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network , 2016, J. Sensors.

[23]  Y. Ouma,et al.  Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery , 2008 .

[24]  H. Zha,et al.  Principal manifolds and nonlinear dimensionality reduction via tangent space alignment , 2004, SIAM J. Sci. Comput..

[25]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[26]  George Lee,et al.  Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene and Protein Expression Studies , 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[27]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[28]  Antonio J. Plaza,et al.  Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Guillermo Sapiro,et al.  Spatially Coherent Nonlinear Dimensionality Reduction and Segmentation of Hyperspectral Images , 2007, IEEE Geoscience and Remote Sensing Letters.

[30]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Rong Jin,et al.  Online Multiple Kernel Classification , 2013, Machine Learning.

[32]  Xuelong Li,et al.  Patch Alignment for Dimensionality Reduction , 2009, IEEE Transactions on Knowledge and Data Engineering.