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Hyperspectral Image Classification via Kernel Sparse Representation

Yi Chen, Nasser M. Nasrabadi, Trac D. Tran
2013 IEEE Transactions on Geoscience and Remote Sensing  
In this paper, a novel nonlinear technique for hyperspectral image classification is proposed.  ...  Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improves the data separability between different classes, providing a higher classification accuracy  ...  Bioucas-Dias for providing the sparse multinomial logistic regression code.  ... 
doi:10.1109/tgrs.2012.2201730 fatcat:t4c7dnj2ijevdk5zgcrknzjgda

Hyperspectral image classification via kernel sparse representation

Yi Chen, Nasser M. Nasrabadi, Trac D. Tran
2011 2011 18th IEEE International Conference on Image Processing  
In this paper, a novel nonlinear technique for hyperspectral image classification is proposed.  ...  Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improves the data separability between different classes, providing a higher classification accuracy  ...  Bioucas-Dias for providing the sparse multinomial logistic regression code.  ... 
doi:10.1109/icip.2011.6115655 dblp:conf/icip/ChenNT11 fatcat:66eupitgsffk3mcqvdeqntt6e4

Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy

S. Prasad, W. Liao, M. He, J. Chanussot
2018 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In Gan et al. a weighted kernel sparse representation model is developed for hyperspectral classification.  ...  Zaouali et al. integrate three-dimensional (3-D) shearlet transforms with Joint Sparse Representation for hyperspectral classification.  ...  In Gan et al. a weighted kernel sparse representation model is developed for hyperspectral classification.  ... 
doi:10.1109/jstars.2018.2820938 fatcat:pqu6zhrl3rc3tm7tqpi4p4t34m

Spectral-spatial hyperspectral classification via shape-adaptive sparse representation

Wei Fu, Shutao Li, Leyuan Fang, Xudong Kang, Jon Atli Benediktsson
2014 2014 IEEE Geoscience and Remote Sensing Symposium  
This paper proposes a new spectral-spatial hyperspectral classification method named the shape-adaptive sparse representation (SASR).  ...  Furthermore, the hyperspectral classification is implemented by incorporating the spatial contextual information of HSI into the sparse representation classification model.  ...  [3] applied the sparse representation technique to hyperspectral classification and proposed the joint sparse representation classification (JSRC) method.  ... 
doi:10.1109/igarss.2014.6947219 dblp:conf/igarss/FuLFKB14 fatcat:6h5aumb6iva7nfvpplbqpk7vc4

A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES

S. Srinivasan, Dr. K. Rajakumar
2017 International Journal on Smart Sensing and Intelligent Systems  
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification.  ...  The spectral and spatial information reflected from the original Hyperspectral Images with four various features.  ...  Fang et al., has stated an efficient tool named sparse representation for Hyperspectral Image Classification (HIC).  ... 
doi:10.21307/ijssis-2017-224 fatcat:k2x24hgfkjctxh3jwjssq5esle

Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models

Umamahesh Srinivas, Yi Chen, Vishal Monga, Nasser Nasrabadi, Trac Tran
2012 IEEE Geoscience and Remote Sensing Letters  
A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra  ...  A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood.  ...  ACKNOWLEDGMENT The authors would like to thank the University of Pavia and the HySenS project for kindly providing the ROSIS images of University of Pavia and Center of Pavia.  ... 
doi:10.1109/lgrs.2012.2211858 fatcat:bhtejgim2fh6rlzn627xeljtye

Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit? [article]

Minshan Cui, Saurabh Prasad
2016 arXiv   pre-print
Based on these developments, a sparse representation-based classification (SRC) has been proposed for a variety of classification and related tasks, including face recognition.  ...  Recently, a class dependent variant of SRC was proposed to overcome the limitations of SRC for remote sensing image classification.  ...  Conclusion In this paper, we present a class-dependent OLS-based classification method named cdOLS for the problem of hyperspectral image classification. We also extend cdOLS into its kernel variant.  ... 
arXiv:1607.04942v1 fatcat:ye25q42ojbb3jhy43by25j4aje

Parallel Spatial–Spectral Hyperspectral Image Classification With Sparse Representation and Markov Random Fields on GPUs

Zebin Wu, Qicong Wang, Antonio Plaza, Jun Li, Le Sun, Zhihui Wei
2015 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Spatial-spectral classification is a very important topic in the field of remotely sensed hyperspectral imaging.  ...  Index Terms-Compute unified device architecture (CUDA), graphics processing units (GPUs), hyperspectral imaging, Markov random fields (MRFs), parallel implementation, sparse representation, spatial-spectral  ...  This classifier models the likelihood probability via an l 1 − l 2 sparse representation method, and the spatial prior is modeled as a Gibbs distribution, which specifies an MRF on the classification labels  ... 
doi:10.1109/jstars.2015.2413931 fatcat:c2ra47klgbgbvbb2tmojqqguke

Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification

Zhen-tao Qin, Wu-nian Yang, Ru Yang, Xiang-yu Zhao, Teng-jiao Yang
2015 Journal of Spectroscopy  
The sparse coefficients are then used to classify the hyperspectral images via a linear SVM.  ...  We calculated the image's sparse coefficients using the dictionary approach, which generated the sparse representation features of the remote sensing images.  ...  sparse representation method for hyperspectral image classification [14] , in which the sparse representation coefficients y are considered to be independent of each other.  ... 
doi:10.1155/2015/678765 fatcat:6pzuhj6zzzdljm6cwzszk4avcm

Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

Yidong Tang, Shucai Huang, Aijun Xue
2016 Mathematical Problems in Engineering  
The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification.  ...  Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper.  ...  Sparse Representation Based Classification 2.1. SRC.  ... 
doi:10.1155/2016/3460281 fatcat:x4ibit2ejbbwnltjqvqhwq62l4

Spatial-Aware Dictionary Learning for Hyperspectral Image Classification [article]

Ali Soltani-Farani, Hamid R. Rabiee, Seyyed Abbas Hosseini
2013 arXiv   pre-print
Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification.  ...  The sparse coefficients are then used for classification using a linear SVM.  ...  Camps-Valls of the University of Valencia, Spain, for helpful discussions regarding the composite kernel SVM.  ... 
arXiv:1308.1187v1 fatcat:4jnynr5e5fhm7dzi6qwy7mna6m

Table of Contents

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Jeon 1905 Kernel Low- Hyperspectral and LiDAR Data Classification Using Kernel Collaborative Representation Based Residual Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  An 1933 Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2019.2928167 fatcat:pu57mfmfurhifl43bwznir2ove

Table of contents

2020 IEEE Geoscience and Remote Sensing Letters  
Zhu 1593 Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries ............................ ........................................................................  ...  Intelligent and Cognitive Computing for Remote Sensing Image Acquisition and Interpretation Multiscale Superpixel Kernel-Based Low-Rank Representation for Hyperspectral Image Classification ...........  ... 
doi:10.1109/lgrs.2020.3016456 fatcat:jdpckutnjfd45dhp4j5pudigwm

Unsupervised deep feature extraction of hyperspectral images

Adriana Romero, Carlo Gatta, Gustavo Camps-Valls
2014 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)  
Index Terms-Convolutional networks, deep learning, sparse learning, feature extraction, hyperspectral image classification * The work of A.  ...  This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images.  ...  : discriminative dictionaries have been proposed for spatial-spectral sparse-representation and image classification [9] , sparse kernel networks have been recently introduced for classification [10]  ... 
doi:10.1109/whispers.2014.8077647 dblp:conf/whispers/RomeroGC14 fatcat:2wpbpmki55bevfm22mp3vajmfq

Manifold Sparse Coding Based Hyperspectral Image Classification

Yanbin Peng, Zhijun Zheng, Jiming Li, Zhigang Pan, Xiaoyong Li, Zhinian Zhai
2016 International Journal of Signal Processing, Image Processing and Pattern Recognition  
Hyperspectral image classification has received an increasing amount of interest in recent years.  ...  Finally, LASSO regularization is used to obtain sparse representation of data projection.  ...  Chen [2] proposed a new algorithm for hyperspectral image classification based on sparse representation.  ... 
doi:10.14257/ijsip.2016.9.12.27 fatcat:iwibk5r3arcnti47m4qiourfpy
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