Superpixel Tensor Sparse Coding for Structural Hyperspectral Image Classification

In this paper, a superpixel tensor sparse coding (STSC) based hyperspectral image classification (HIC) method is proposed, by exploring the high-order structure of hyperspectral image and utilizing information along all dimensions to better understand data. First, a hierarchical spatial affinity propagation algorithm is developed to rapidly cluster the image into multiple superpixels tensors. Then, a new STSC-based classifier followed by hybrid pixel-superpixel ensemble strategy is constructed for HIC. Because superpixels can reduce the misclassification caused by mixed pixel and tensor sparse coding can simultaneously classify multiple superpixels, rapid and accurate HIC can be achieved. Some experiments are taken on several datasets, and the results show the superiority of STSC to its counterparts in terms of speed and accuracy.

[1]  Qian Du,et al.  Collaborative-Representation-Based Nearest Neighbor Classifier for Hyperspectral Imagery , 2015, IEEE Geoscience and Remote Sensing Letters.

[2]  Rui Zhang,et al.  Semi-Supervised Hyperspectral Image Classification Using Spatio-Spectral Laplacian Support Vector Machine , 2014, IEEE Geoscience and Remote Sensing Letters.

[3]  Aakanksha Rana,et al.  Graph-cut-based model for spectral-spatial classification of hyperspectral images , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[4]  Andrzej Cichocki,et al.  Computing Sparse Representations of Multidimensional Signals Using Kronecker Bases , 2013, Neural Computation.

[5]  Joydeep Ghosh,et al.  Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Luis Gómez-Chova,et al.  Semisupervised Image Classification With Laplacian Support Vector Machines , 2008, IEEE Geoscience and Remote Sensing Letters.

[8]  Bin Li,et al.  Semisupervised Dual-Geometric Subspace Projection for Dimensionality Reduction of Hyperspectral Image Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Shuyuan Yang,et al.  Hyperspectral Image Classification Based on Relaxed Clustering Assumption and Spatial Laplace Regularizer , 2014, IEEE Geoscience and Remote Sensing Letters.

[10]  John A. Richards,et al.  Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[11]  Jon Atli Benediktsson,et al.  Classification of multisource and hyperspectral data based on decision fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[12]  Glenn Healey,et al.  Markov Random Field Models for Unsupervised Segmentation of Textured Color Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Horst Bischof,et al.  Semi-supervised image classification with huberized Laplacian Support Vector Machines , 2013, 2013 IEEE 9th International Conference on Emerging Technologies (ICET).

[14]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[15]  Brendan J. Frey,et al.  Hierarchical Affinity Propagation , 2011, UAI.

[16]  Bor-Chen Kuo,et al.  Kernel-Based KNN and Gaussian Classifiers for Hyperspectral Image Classification , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[17]  George A. Vouros,et al.  Synthesizing Ontology Alignment Methods Using the Max-Sum Algorithm , 2012, IEEE Transactions on Knowledge and Data Engineering.

[18]  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.

[19]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Qian Du,et al.  Combined sparse and collaborative representation for hyperspectral target detection , 2015, Pattern Recognit..

[21]  Antonio J. Plaza,et al.  Probabilistic-Kernel Collaborative Representation for Spatial–Spectral Hyperspectral Image Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

[23]  Jon Atli Benediktsson,et al.  Generalized Composite Kernel Framework for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  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.

[25]  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.

[26]  Li Ping,et al.  The Factor Graph Approach to Model-Based Signal Processing , 2007, Proceedings of the IEEE.

[27]  Saurabh Prasad,et al.  Class-Dependent Sparse Representation Classifier for Robust Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Rama Chellappa,et al.  Entropy rate superpixel segmentation , 2011, CVPR 2011.

[29]  Liang Xiao,et al.  Supervised Spectral–Spatial Hyperspectral Image Classification With Weighted Markov Random Fields , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Antonio J. Plaza,et al.  Complementarity of Discriminative Classifiers and Spectral Unmixing Techniques for the Interpretation of Hyperspectral Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[31]  A. Kai Qin,et al.  Collaborative Active and Semisupervised Learning for Hyperspectral Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[33]  James E. Fowler,et al.  Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields , 2014, IEEE Geoscience and Remote Sensing Letters.

[34]  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.

[35]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[36]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[37]  Joydeep Ghosh,et al.  An Active Learning Approach to Hyperspectral Data Classification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Xing Zhao,et al.  Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  Liang Xiao,et al.  Hyperspectral Image Classification Using Kernel Sparse Representation and Semilocal Spatial Graph Regularization , 2014, IEEE Geoscience and Remote Sensing Letters.

[40]  Gustavo Camps-Valls,et al.  Semi-Supervised Graph-Based Hyperspectral Image Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

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

[42]  Trac D. Tran,et al.  Hyperspectral Image Classification via Kernel Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Jon Atli Benediktsson,et al.  Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

[45]  Eduardo Bayro-Corrochano,et al.  Clifford Support Vector Machines for Classification, Regression, and Recurrence , 2010, IEEE Transactions on Neural Networks.

[46]  Qian Du,et al.  Kernel Collaborative Representation With Tikhonov Regularization for Hyperspectral Image Classification , 2014, IEEE Geoscience and Remote Sensing Letters.

[47]  Luis Alonso,et al.  Robust support vector method for hyperspectral data classification and knowledge discovery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

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

[49]  Abel G. Silva-Filho,et al.  Hyperspectral images clustering on reconfigurable hardware using the k-means algorithm , 2003, 16th Symposium on Integrated Circuits and Systems Design, 2003. SBCCI 2003. Proceedings..

[50]  Shuyuan Yang,et al.  Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification , 2014, IEEE Geoscience and Remote Sensing Letters.

[51]  Hongbing Ma,et al.  Classification of Hyperspectral Image Based on Sparse Representation in Tangent Space , 2015, IEEE Geoscience and Remote Sensing Letters.

[52]  Peng Li,et al.  Compressive Hyperspectral Imaging via Sparse Tensor and Nonlinear Compressed Sensing , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[53]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[54]  Yuan Yan Tang,et al.  Hyperspectral Image Classification Based on Regularized Sparse Representation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[55]  Hamid R. Rabiee,et al.  Spatial-Aware Dictionary Learning for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[56]  Liangpei Zhang,et al.  A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[57]  Qian Du,et al.  Hyperspectral Image Classification Using Weighted Joint Collaborative Representation , 2015, IEEE Geoscience and Remote Sensing Letters.

[58]  Andrzej Cichocki,et al.  Block sparse representations of tensors using Kronecker bases , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[59]  James E. Fowler,et al.  Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

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

[61]  Liang Xiao,et al.  Spatial-Spectral Kernel Sparse Representation for Hyperspectral Image Classification , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.