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Hyperspectral Image Classification via Joint Sparse representation of Multi-layer Superpixles
2018
KSII Transactions on Internet and Information Systems
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In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. ...
Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based ...
superpixles for joint sparse representation classification. ...
doi:10.3837/tiis.2018.10.021
fatcat:3d7qwd2rifdw5fu3w45lr7tlkq
Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy
2018
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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. ...
Zaouali et al. integrate three-dimensional (3-D) shearlet transforms with Joint Sparse Representation for hyperspectral classification. ...
doi:10.1109/jstars.2018.2820938
fatcat:pqu6zhrl3rc3tm7tqpi4p4t34m
Spectral-spatial hyperspectral classification via shape-adaptive sparse representation
2014
2014 IEEE Geoscience and Remote Sensing Symposium
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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. ...
Joint Sparse Representation (SR) Classification The SR of pixels within each SA region around a test pixel is obtained via joint sparse regularization. ...
doi:10.1109/igarss.2014.6947219
dblp:conf/igarss/FuLFKB14
fatcat:6h5aumb6iva7nfvpplbqpk7vc4
Noise reduction of hyperspectral imagery using nonlocal sparse representation with spectral-spatial structure
2012
2012 IEEE International Geoscience and Remote Sensing Symposium
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Sparse representation with spectral-spatial structure can exploit spectral and spatial joint correlations of hyperspectral imagery also making the signal and noise more distinguished, in which 3-D blocks ...
In this paper, we de velop a sparse representation based noise reduction method for hyperspectral imagery, which is dependent on the assump tion that the non-noise component in the signal can be approx ...
In order to further exploit spectral-spatial joint correlations of hyperspectral imagery, we construct the sparse representation of 3-D block instead of 2-D patch or I-D line segment, which is based on ...
doi:10.1109/igarss.2012.6350674
dblp:conf/igarss/QianYW12
fatcat:nnhlslgiqjdnhkffaltsznmyv4
Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models
2012
IEEE Geoscience and Remote Sensing Letters
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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
Spectral–spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder
2017
Journal of Applied Remote Sensing
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A stacked sparse autoencoder provides unsupervised feature learning to extract high-level feature representations of joint spectralspatial information; then, a soft classifier is employed to train high-level ...
A deep learning architecture is proposed to classify hyperspectral remote sensing imagery by joint utilization of spectral-spatial information. ...
Conclusions In this paper, joint spectral-spatial information is exploited in a deep stacked sparse autoencoder for hyperspectral imagery classification. ...
doi:10.1117/1.jrs.11.042604
fatcat:enkwyrpdufefzaxx6swflma2fq
Table of contents
2020
IEEE Geoscience and Remote Sensing Letters
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Zhang 1573 Hyperspectral Data Deep Feature-Based Multitask Joint Sparse Representation for Hyperspectral Image Classification ..................... ..................................................... ...
Zhu 1593 Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries ............................ ........................................................................ ...
doi:10.1109/lgrs.2020.3016456
fatcat:jdpckutnjfd45dhp4j5pudigwm
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
2017
International Journal on Smart Sensing and Intelligent Systems
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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
Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images
2018
Mathematical Problems in Engineering
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Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification ...
At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. ...
Recently, sparse representation has been used for hyperspectral image classification, and achieved certain results [16] [17] [18] [19] [20] [21] . ...
doi:10.1155/2018/8264961
fatcat:jgbe2i2xf5au7b6tib3nuiosve
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification
2014
IEEE Geoscience and Remote Sensing Letters
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2014 pre-print arXiv:1401.3818v1 fatcat:kxd43222mffapkhwlkeawzq27m
Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. ...
In this paper, we review and compare several structured priors for sparse-representation-based HSI classification. ...
HSI CLASSIFICATION VIA DIFFERENT STRUCTURED SPARSE PRIORS
A. Joint Sparsity Prior In HSI, pixels within a small neighborhood usually consist of similar materials. ...
doi:10.1109/lgrs.2013.2290531
fatcat:qxo5mgjuazfdbmuqwtlfjtscsq
Editorial for Special Issue "Hyperspectral Imaging and Applications"
2019
Remote Sensing
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This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification ...
Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. ...
Part IV: Hyperspectral Image Classification (6 papers)
09-00872 Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification
10-00515
Semi-Supervised Classification ...
doi:10.3390/rs11172012
fatcat:c23u3rahgjhctowk5xwllt2qea
Discriminative graphical models for sparsity-based hyperspectral target detection
2012
2012 IEEE International Geoscience and Remote Sensing Symposium
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Fig. 1 . 1 Hyperspectral image detection using discriminative graphical models on sparse feature representations obtained from local pixel neighborhoods. ...
SPONSOR/MONITOR'S REPORT NUMBER(S)
INTRODUCTION An important research problem in hyperspectral imaging (HSI) [1] is hyperspectral target detection, which can be viewed as a binary classification problem ...
doi:10.1109/igarss.2012.6350822
dblp:conf/igarss/SrinivasCMNT12
fatcat:b4733caypvhmtp6mavcugszy4y
Table of contents
2019
IEEE Transactions on Geoscience and Remote Sensing
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Benediktsson 5085 Simultaneous Reconstruction and Anomaly Detection of Subsampled Hyperspectral Images Using l (1/2) Regularized Joint Sparse and Low-Rank Recovery ..................................... ...
Gao 4246 Joint Sparse Aperture ISAR Autofocusing and Scaling via Modified Newton Method-Based Variational Bayesian Inference ............................................................................ ...
doi:10.1109/tgrs.2019.2923179
fatcat:nfaahnqzcvft5ezz36a6nyy2ri
Special Section Guest Editorial: Sparsity Driven High Dimensional Remote Sensing Image Processing and Analysis
2016
Journal of Applied Remote Sensing
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"Temperature and emissivity separation via sparse representation with thermal airborne hyperspectral" by C. ...
"Sparse coding-based correlation model for land-use scene classification in high-resolution remote-sensing images" by K. ...
Finally, according to the sparse representation classification (SRC) paradigm, two papers further explore hyperspectral image pattern recognition. ...
doi:10.1117/1.jrs.10.042001
fatcat:n2s7tfqdozdtfndamcyndhzcwu
Table of contents
2021
IEEE Transactions on Geoscience and Remote Sensing
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Sveinsson 535 Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation ......................................................................... ...
Jonard 151 Sparse Aperture ISAR Imaging Method Based on Joint Constraints of Sparsity and Low Rank ......................... ............................................................................ ...
doi:10.1109/tgrs.2020.3039449
fatcat:sxee5msl55emhnszejgpf6vrpa
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