Spatial-Spectral Graph Regularized Kernel Sparse Representation for Hyperspectral Image Classification

This paper presents a spatial-spectral method for hyperspectral image classification in the regularization framework of kernel sparse representation. First, two spatial-spectral constraint terms are appended to the sparse recovery model of kernel sparse representation. The first one is a graph-based spatially-smooth constraint which is utilized to describe the contextual information of hyperspectral images. The second one is a spatial location constraint, which is exploited to incorporate the prior knowledge of the location information of training pixels. Then, an efficient alternating direction method of multipliers is developed to solve the corresponding minimization problem. At last, the recovered sparse coefficient vectors are used to determine the labels of test pixels. Experimental results carried out on three real hyperspectral images point out the effectiveness of the proposed method.

[1]  Ribana Roscher,et al.  Superpixel-based classification of hyperspectral data using sparse representation and conditional random fields , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[2]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

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

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

[6]  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.

[7]  Nikolaos Doulamis,et al.  Deep supervised learning for hyperspectral data classification through convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

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

[9]  Liang Xiao,et al.  Hyperspectral image classification via region-based composite kernels , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[10]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

[12]  Jie Wang,et al.  Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network , 2017, Remote. Sens..

[13]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

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

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

[16]  Jon Atli Benediktsson,et al.  Segmentation and classification of hyperspectral images using watershed transformation , 2010, Pattern Recognit..

[17]  Xiuping Jia,et al.  Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Junfeng Yang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

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

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

[21]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[22]  Tao Fang,et al.  Selective convolutional neural networks and cascade classifiers for remote sensing image classification , 2017 .

[23]  Yiming Pi,et al.  Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels , 2014, Remote. Sens..

[24]  F. Lehmann,et al.  HyMap hyperspectral remote sensing to detect hydrocarbons , 2001 .

[25]  Jon Atli Benediktsson,et al.  Morphological Attribute Profiles for the Analysis of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Patrick L. Combettes,et al.  Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..

[27]  Liang-Tien Chia,et al.  Kernel Sparse Representation for Image Classification and Face Recognition , 2010, ECCV.

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

[29]  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.