Efficient Superpixel-Level Multitask Joint Sparse Representation for Hyperspectral Image Classification

In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers.

[1]  Yuan Tian,et al.  Local Patch Discriminative Metric Learning for Hyperspectral Image Feature Extraction , 2014, IEEE Geoscience and Remote Sensing Letters.

[2]  Jing Wang,et al.  Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Antonio J. Plaza,et al.  Sparse Unmixing of Hyperspectral Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Lorenzo Bruzzone,et al.  Extended profiles with morphological attribute filters for the analysis of hyperspectral data , 2010 .

[5]  Liangpei Zhang,et al.  An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Licheng Jiao,et al.  Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Simon C. K. Shiu,et al.  Image Set-Based Collaborative Representation for Face Recognition , 2013, IEEE Transactions on Information Forensics and Security.

[8]  Pedram Ghamisi,et al.  Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

[10]  Bo Du,et al.  Semisupervised Discriminative Locally Enhanced Alignment for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Qian Du,et al.  Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[12]  J. Anthony Gualtieri,et al.  Support vector machines for hyperspectral remote sensing classification , 1999, Other Conferences.

[13]  Xi Chen,et al.  Accelerated Gradient Method for Multi-task Sparse Learning Problem , 2009, 2009 Ninth IEEE International Conference on Data Mining.

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

[15]  Jun Zhou,et al.  Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Shuicheng Yan,et al.  Visual classification with multi-task joint sparse representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Antonio J. Plaza,et al.  New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Liangpei Zhang,et al.  Supervised Segmentation of Very High Resolution Images by the Use of Extended Morphological Attribute Profiles and a Sparse Transform , 2014, IEEE Geoscience and Remote Sensing Letters.

[19]  David J. Crisp,et al.  A Geometric Interpretation of v-SVM Classifiers , 1999, NIPS.

[20]  Ye Zhang,et al.  Robust Hyperspectral Classification Using Relevance Vector Machine , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[21]  S. Prasher,et al.  Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn , 2003 .

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

[23]  Trac D. Tran,et al.  Sparse Representation for Target Detection in Hyperspectral Imagery , 2011, IEEE Journal of Selected Topics in Signal Processing.

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

[25]  David Zhang,et al.  Collaborative Representation based Classification for Face Recognition , 2012, ArXiv.

[26]  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 .

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

[28]  Nasser M. Nasrabadi,et al.  Automated Hyperspectral Cueing for Civilian Search and Rescue , 2009, Proceedings of the IEEE.

[29]  William J. Byrne,et al.  Convergence Theorems for Generalized Alternating Minimization Procedures , 2005, J. Mach. Learn. Res..

[30]  Heesung Kwon,et al.  Sparse Kernel-Based Ensemble Learning With Fully Optimized Kernel Parameters for Hyperspectral Classification Problems , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Lorenzo Bruzzone,et al.  A novel classification technique based on progressive transductive SVM learning , 2014, Pattern Recognit. Lett..

[32]  Kristin P. Bennett,et al.  Duality and Geometry in SVM Classifiers , 2000, ICML.

[33]  Liangpei Zhang,et al.  Hyperspectral Image Classification by Nonlocal Joint Collaborative Representation With a Locally Adaptive Dictionary , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

[35]  Gustavo Camps-Valls,et al.  Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.

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

[37]  Antonio J. Plaza,et al.  Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Tim R. McVicar,et al.  Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..

[39]  Xin Huang,et al.  A multilevel decision fusion approach for urban mapping using very high-resolution multi/hyperspectral imagery , 2012 .

[40]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[43]  Hongyan Zhang,et al.  Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification , 2014 .

[44]  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.

[45]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression , 2013, IEEE Geoscience and Remote Sensing Letters.

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

[47]  Liangpei Zhang,et al.  A Nonlinear Multiple Feature Learning Classifier for Hyperspectral Images With Limited Training Samples , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[48]  Qian Du,et al.  Optimized Hyperspectral Band Selection Using Particle Swarm Optimization , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[49]  Ben Taskar,et al.  Joint covariate selection and joint subspace selection for multiple classification problems , 2010, Stat. Comput..

[50]  Melba M. Crawford,et al.  Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning , 2014, IEEE Signal Processing Magazine.

[51]  Liangpei Zhang,et al.  Joint Collaborative Representation With Multitask Learning for Hyperspectral Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.