Hyperspectral image classification via compact-dictionary-based sparse representation

In this paper, a compact-dictionary-based sparse representation (CDSR) method is proposed for hyperspectral image (HSI) classification. The proposed dictionary in CDSR is dynamically generated according to the spatial and spectral context of each pixel. It can effectively shrink the decision range for classification, and reduce the computational burden since the compact dictionary is composed of the classes correlated with the target pixel in terms of spatial location and spectral information. In order to obtain better spatial context information, a spatial location expanding strategy is designed for spreading local explicit label information to a wider region. Experimental results demonstrate the effectiveness and superiority of the proposed method when compared with some widely used HSI classification approaches.

[1]  Jiangtao Peng,et al.  Nearest Regularized Joint Sparse Representation for Hyperspectral Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

[2]  Qi Tian,et al.  Sequential Video VLAD: Training the Aggregation Locally and Temporally , 2018, IEEE Transactions on Image Processing.

[3]  Bing Zhang,et al.  Application of hyperspectral remote sensing for environment monitoring in mining areas , 2012, Environmental Earth Sciences.

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

[5]  Trac D. Tran,et al.  Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification , 2014, IEEE Geoscience and Remote Sensing Letters.

[6]  Licheng Jiao,et al.  Application of a homogenous patch mean kernel with within-class collaborative representation for hyperspectral imagery classification , 2017 .

[7]  Lei Guo,et al.  Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Di Xiao,et al.  An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules , 2014, Multimedia Tools and Applications.

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

[10]  Shuyuan Yang,et al.  Self-Paced Learning-Based Probability Subspace Projection for Hyperspectral Image Classification , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Juha Suomalainen,et al.  Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Jon Atli Benediktsson,et al.  Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.

[13]  Jung-San Lee,et al.  Selective scalable secret image sharing with verification , 2015, Multimedia Tools and Applications.

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

[15]  Thomas S. Huang,et al.  Spatial–Spectral Classification of Hyperspectral Images Using Discriminative Dictionary Designed by Learning Vector Quantization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Jon Atli Benediktsson,et al.  Hyperspectral Image Classification Via Shape-Adaptive Joint Sparse Representation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[18]  Ribana Roscher,et al.  Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Daniela I. Moody,et al.  Automatic detection of pulsed radio frequency (RF) targets using sparse representations in undercomplete learned dictionaries , 2014, Defense + Security Symposium.

[20]  Changxin Gao,et al.  A Discriminant Sparse Representation Graph-Based Semi-Supervised Learning for Hyperspectral Image Classification , 2015, CCCV.

[21]  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.

[22]  Qi Tian,et al.  Image Annotation by Input–Output Structural Grouping Sparsity , 2012, IEEE Transactions on Image Processing.

[23]  Liang Xiao,et al.  Superpixel-guided multiscale kernel collaborative representation for hyperspectral image classification , 2016 .

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

[25]  Kazhong Deng,et al.  Strategies Combining Spectral Angle Mapper and Change Vector Analysis to Unsupervised Change Detection in Multispectral Images , 2016, IEEE Geoscience and Remote Sensing Letters.

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

[27]  Ping Zhong,et al.  Active Learning With Gaussian Process Classifier for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Richard A. Hallett,et al.  Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies , 2008 .

[29]  Chen Li,et al.  Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Yueting Zhuang,et al.  Sparse Unsupervised Dimensionality Reduction for Multiple View Data , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[31]  René Vidal,et al.  Sparse subspace clustering , 2009, CVPR.

[32]  Qingquan Li,et al.  Superpixel-Based Multitask Learning Framework for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Yong Bai,et al.  A remote sensing image classification method based on sparse representation , 2016, Multimedia Tools and Applications.

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

[35]  Trac D. Tran,et al.  Task-Driven Dictionary Learning for Hyperspectral Image Classification With Structured Sparsity Constraints , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[36]  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 .

[37]  Chandan Chakraborty,et al.  Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation , 2018, IEEE Transactions on Image Processing.

[38]  Liangpei Zhang,et al.  Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[39]  George A. Lampropoulos,et al.  Hyperspectral Classification Fusion for Classifying Different Military Targets , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[40]  Yi Yang,et al.  Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[41]  Yuan Yan Tang,et al.  Sparse Representation Based on Set-to-Set Distance for Hyperspectral Image Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  Gang Hua,et al.  Hyperspectral Image Classification Through Bilayer Graph-Based Learning , 2014, IEEE Transactions on Image Processing.

[43]  Jun Li,et al.  Advanced Spectral Classifiers for Hyperspectral Images: A review , 2017, IEEE Geoscience and Remote Sensing Magazine.

[44]  Peijun Du,et al.  Foreword to the special issue on hyperspectral remote sensing: Theory, methods, and applications , 2013 .

[45]  Xia Xu,et al.  R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.