On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification

In hyperspectral remote sensing image classification, multiple features, e.g., spectral, texture, and shape features, are employed to represent pixels from different perspectives. It has been widely acknowledged that properly combining multiple features always results in good classification performance. In this paper, we introduce the patch alignment framework to linearly combine multiple features in the optimal way and obtain a unified low-dimensional representation of these multiple features for subsequent classification. Each feature has its particular contribution to the unified representation determined by simultaneously optimizing the weights in the objective function. This scheme considers the specific statistical properties of each feature to achieve a physically meaningful unified low-dimensional representation of multiple features. Experiments on the classification of the hyperspectral digital imagery collection experiment and reflective optics system imaging spectrometer hyperspectral data sets suggest that this scheme is effective.

[1]  Meng Wang,et al.  Toward the Optimization of Normalized Graph Laplacian , 2011, IEEE Transactions on Neural Networks.

[2]  Jakob J. Verbeek,et al.  Learning nonlinear image manifolds by global alignment of local linear models , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Stefano Pignatti,et al.  Experimental Approach to the Selection of the Components in the Minimum Noise Fraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Li Ma,et al.  Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Chein-I. Chang.  Hyperspectral Data Exploitation: Theory and Applications , 2007 .

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

[7]  Luis O. Jimenez-Rodriguez,et al.  Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Bo Du,et al.  Hybrid Detectors Based on Selective Endmembers , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Liangpei Zhang,et al.  An Adaptive Multiscale Information Fusion Approach for Feature Extraction and Classification of IKONOS Multispectral Imagery Over Urban Areas , 2007, IEEE Geoscience and Remote Sensing Letters.

[10]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[11]  Liangpei Zhang,et al.  An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[12]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  Jon Atli Benediktsson,et al.  Fusion of Support Vector Machines for Classification of Multisensor Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Josiane Zerubia,et al.  Texture feature analysis using a gauss-Markov model in hyperspectral image classification , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Kaizhu Huang,et al.  m-SNE: Multiview Stochastic Neighbor Embedding , 2011, IEEE Trans. Syst. Man Cybern. Part B.

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

[18]  Chein-I Chang,et al.  Hyperspectral Data Exploitation , 2007 .

[19]  Jocelyn Chanussot,et al.  Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Lorenzo Bruzzone,et al.  A Novel Approach to the Selection of Spatially Invariant Features for the Classification of Hyperspectral Images With Improved Generalization Capability , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Liangpei Zhang,et al.  A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Liangpei Zhang,et al.  A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution , 2007, IEEE Transactions on Image Processing.

[24]  Dacheng Tao,et al.  Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Xindong Wu,et al.  Manifold elastic net: a unified framework for sparse dimension reduction , 2010, Data Mining and Knowledge Discovery.

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

[27]  Curt H. Davis,et al.  A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

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

[29]  Xin Huang,et al.  A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform , 2008 .

[30]  Liangpei Zhang,et al.  Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Emmanuel Arzuaga-Cruz,et al.  Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

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

[33]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[34]  Glenn Healey,et al.  Hyperspectral Texture Synthesis Using Histogram and Power Spectral Density Matching , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Demetris Stathakis,et al.  Feature Evolution for Classification of Remotely Sensed Data , 2007, IEEE Geoscience and Remote Sensing Letters.

[36]  Lorenzo Bruzzone,et al.  A new search algorithm for feature selection in hyperspectral remote sensing images , 2001, IEEE Trans. Geosci. Remote. Sens..

[37]  Bor-Chen Kuo,et al.  Hyperspectral Image Classification Using Kernel-based Nonparametric Weighted Feature Extraction , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[38]  Jinwen Tian,et al.  Generalised supervised local tangent space alignment for hyperspectral image classification , 2010 .

[39]  Anil K. Jain,et al.  Incremental nonlinear dimensionality reduction by manifold learning , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Glenn Healey,et al.  Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Bor-Chen Kuo,et al.  Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Xin Huang,et al.  A Multidirectional and Multiscale Morphological Index for Automatic Building Extraction from Multispectral GeoEye-1 Imagery , 2011 .

[43]  Liangpei Zhang,et al.  A Multifeature Tensor for Remote-Sensing Target Recognition , 2011, IEEE Geoscience and Remote Sensing Letters.

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

[45]  Ram M. Narayanan,et al.  Integrated spectral and spatial information mining in remote sensing imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[47]  James M. Keller,et al.  Automated Geospatial Conflation of Vector Road Maps to High Resolution Imagery , 2009, IEEE Transactions on Image Processing.

[48]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[49]  Joydeep Ghosh,et al.  Best-bases feature extraction algorithms for classification of hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[50]  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.