Edge-preserving-based collaborative representation for spectral-spatial classification

ABSTRACT Recently, the nearest regularized subspace (NRS) classifier and its spectral–spatial versions such as joint collaborative representation (JCR) and weighted JCR (WJCR) have gained an increasing interest in the hyperspectral image classification. JCR and WJCR average each pixel with its neighbours in a spatial neighbourhood window. The use of spatial information as averaging of pixels in a local window may degrade the classification accuracy in the neighbourhood of discontinuities and class boundaries. We propose the edge-preserving-based collaborative representation (EPCR) classifier in this article, which overcomes this problem by using the edge image estimated by the original full-band hyperspectral image. The estimated edge image is used for calculation of the weights of neighbours and also the final residuals in the collaborative representation classifier. The advantage of multiscale spatial window is also assessed in this work. Moreover, the kernelized versions of NRS and its improved versions are developed in this article. Our experimental results on several popular hyperspectral images indicate that EPCR and its kernelized version are superior to some state-of-the-art classification methods.

[1]  Lorenzo Bruzzone,et al.  Toward the Automatic Updating of Land-Cover Maps by a Domain-Adaptation SVM Classifier and a Circular Validation Strategy , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Tülay Adali,et al.  Spectral–Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

[4]  Hassan Ghassemian,et al.  Rational function approximation for feature reduction in hyperspectral data , 2016 .

[5]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[6]  Yuliya Tarabalka,et al.  Best Merge Region-Growing Segmentation With Integrated Nonadjacent Region Object Aggregation , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Hassan Ghassemian,et al.  Integrating Hierarchical Segmentation Maps With MRF Prior for Classification of Hyperspectral Images in a Bayesian Framework , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

[9]  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.

[10]  Ying Wang,et al.  Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation , 2014 .

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

[12]  Melba M. Crawford,et al.  Manifold learning based feature extraction for classification of hyper-spectral data , 2013 .

[13]  Peijun Du,et al.  Combining Morphological Attribute Profiles via an Ensemble Method for Hyperspectral Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

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

[15]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[16]  Hassan Ghassemian,et al.  Enhancing Hyperspectral Endmember Extraction Using Clustering and Oversegmentation-Based Preprocessing , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[18]  Ye Zhang,et al.  Representative Multiple Kernel Learning for Classification in Hyperspectral Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Hassan Ghassemian,et al.  Nonparametric feature extraction for classification of hyperspectral images with limited training samples , 2016 .

[20]  Jon Atli Benediktsson,et al.  Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

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

[23]  Jon Atli Benediktsson,et al.  Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[24]  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.

[25]  Hassan Ghassemian,et al.  An Adaptive Pixon Extraction Technique for Multispectral/Hyperspectral Image Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[26]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Jon Atli Benediktsson,et al.  Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Jon Atli Benediktsson,et al.  Nonlinear Multiple Kernel Learning With Multiple-Structure-Element Extended Morphological Profiles for Hyperspectral Image Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

[31]  Yanfeng Gu,et al.  Superpixel-Based Intrinsic Image Decomposition of Hyperspectral Images , 2017, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[34]  James E. Fowler,et al.  Nearest Regularized Subspace for Hyperspectral Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Maryam Imani,et al.  Binary coding based feature extraction in remote sensing high dimensional data , 2016, Inf. Sci..

[36]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[37]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[38]  Qian Du,et al.  Joint Within-Class Collaborative Representation for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  H. Ghassemian,et al.  Feature space discriminant analysis for hyperspectral data feature reduction , 2015 .

[40]  Anne H. Schistad Solberg,et al.  Structured Gaussian Components for Hyperspectral Image Classification , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Qingquan Li,et al.  A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection , 2016, IEEE Transactions on Geoscience and Remote Sensing.