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Manifold Sparse Coding Based Hyperspectral Image Classification
2016
International Journal of Signal Processing, Image Processing and Pattern Recognition
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Finally, LASSO regularization is used to obtain sparse representation of data projection. ...
Hyperspectral image classification has received an increasing amount of interest in recent years. ...
Chen [2] proposed a new algorithm for hyperspectral image classification based on sparse representation. ...
doi:10.14257/ijsip.2016.9.12.27
fatcat:iwibk5r3arcnti47m4qiourfpy
Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm
2017
Sensors
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Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. ...
However, current sparse representation based classification models have not considered all the test pixels simultaneously. ...
Chen et al. introduced a dictionary-based sparse representation framework for hyperspectral classification [15] . ...
doi:10.3390/s17020314
fatcat:kwmuzsjmijcmbn7e2htpyev4lq
Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification
2015
Journal of Spectroscopy
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Experiments show that our proposed method of dictionary-based, clustered sparse coefficients can create better representations of hyperspectral images, with a greater overall accuracy and a Kappa coefficient ...
This paper presents a new, dictionary-based method for hyperspectral image classification, which incorporates both spectral and contextual characteristics of a sample clustered to obtain a dictionary of ...
Sincere thanks are due to Soltani-Farani A and Paolo Gamba for giving one of the authors a very friendly help. ...
doi:10.1155/2015/678765
fatcat:6pzuhj6zzzdljm6cwzszk4avcm
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. ...
Rajakumar A review on multiple-feature-based adaptive sparse representation (MFASR) and other classification types in Hyperspectral Image is denoted by 'X', which is referred as X=[X 1 , X 2 …X N ] ε R ...
doi:10.21307/ijssis-2017-224
fatcat:k2x24hgfkjctxh3jwjssq5esle
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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Wang et al. present an approach to hyperspectral image restoration based on total variation regularized low-rank tensor decomposition. ...
In Gan et al. a weighted kernel sparse representation model is developed for hyperspectral classification. ...
Wang et al. present an approach to hyperspectral image restoration based on total variation regularized low-rank tensor decomposition. ...
doi:10.1109/jstars.2018.2820938
fatcat:pqu6zhrl3rc3tm7tqpi4p4t34m
HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON MANIFOLD DATA ANALYSIS AND SPARSE SUBSPACE PROJECTION
2021
International Journal of Engineering Technologies and Management Research
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Aiming at the problem of "dimension disaster" in hyperspectral image classification, a method of dimension reduction based on manifold data analysis and sparse subspace projection (MDASSP) is proposed. ...
The new method combines sparse coding and manifold learning to generate features with better classification ability. ...
Yuan (2021) ], this paper proposes a hyperspectral image classification method based on Manifold Data Analysis and Sparse Subspace Projection (MDASSP). ...
doi:10.29121/ijetmr.v8.i9.2021.1040
fatcat:ba6j54sypfbjrnebr4v6xj36rm
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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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 ...
Noise reduction is always an active research area in image processing due to its importance for the sequential tasks such as object classification and detection. ...
In this paper, we focus on sparse representation based noise reduction with spectral-spatial structure. ...
doi:10.1109/igarss.2012.6350674
dblp:conf/igarss/QianYW12
fatcat:nnhlslgiqjdnhkffaltsznmyv4
Hyperspectral Remote Sensing Image Classification with CNN Based on Quantum Genetic-Optimized Sparse Representation
2020
IEEE Access
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INDEX TERMS Hyperspectral remote sensing, image classification, sparse representation, convolutional neural network, quantum genetic. ...
The comparison results show that the QGASR-CNN sparsely represents the features of hyperspectral remote sensing images and improves the classification accuracy. ...
A new hyperspectral remote sensing image classification method based on sparse representation with quantum genetic algorithm and convolutional neural network, namely QGASR-CNN is proposed in this paper ...
doi:10.1109/access.2020.2997912
fatcat:fwubjuutkjgupnghggezq7r5ra
Hyperspectral Image Classification Using Geodesic Spatial–Spectral Collaborative Representation
2023
Electronics
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To address this, this paper introduces a novel approach to hyperspectral image classification based on geodesic spatial–spectral collaborative representation. ...
The effective and comprehensive utilization of spatial and spectral information to achieve the accurate classification of hyperspectral images presents a significant challenge in the domain of hyperspectral ...
The sparse representation classifier based on the l 1 norm directly converts the hyperspectral image classification problem into a convex optimization problem by minimizing the l 1 norm. ...
doi:10.3390/electronics12183777
fatcat:wn2csavfyne2fdoyzb7com7eqa
An analysis of collaborative representation schemes for the classification of hyperspectral images
2015
2015 23rd European Signal Processing Conference (EUSIPCO)
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Specifically, we focus on collaborative and sparse representation classifiers and we perform an investigation on the role of the different regularizations and constraints that can be considered with respect ...
In this paper, we consider these approaches for the hyperspectral image classification. ...
Classifiers based on sparse representations (named Sparse Representation Classifiers, SRC) have been proven their effectiveness in several applicative domains of signal and image processing [1] . ...
doi:10.1109/eusipco.2015.7362484
dblp:conf/eusipco/MuraBC15
fatcat:4vpsmme56ffpliuitmcebx4iiy
Mixed Poisson-Gaussian noise model based sparse denoising for hyperspectral imagery
2012
2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)
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Sparse representation has been applied to image denoising in recent years. ...
It is based on the assumption that the non-noise component in the signal can be approximated by only a small number of atoms in a dictionary while the noise component cannot. ...
There fore, most of popular image denoising methods including sparse representation based algorithm cannot be directly ap plied for hyperspectral imagery. ...
doi:10.1109/whispers.2012.6874280
dblp:conf/whispers/YeQ12
fatcat:bsln5kssjbb43jsqr5i2uxmcpa
Special Section Guest Editorial: Sparsity Driven High Dimensional Remote Sensing Image Processing and Analysis
2016
Journal of Applied Remote Sensing
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"Sparse coding-based correlation model for land-use scene classification in high-resolution remote-sensing images" by K. ...
"Temperature and emissivity separation via sparse representation with thermal airborne hyperspectral" by C. ...
"Local-preserving sparse representation-based classification in hyperspectral imagery" by L. ...
doi:10.1117/1.jrs.10.042001
fatcat:n2s7tfqdozdtfndamcyndhzcwu
A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples
2012
IEEE Transactions on Geoscience and Remote Sensing
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In this paper, we exploit certain special properties of hyperspectral data and propose an 1 -minimization-based sparse representation classification approach to overcome this difficulty in hyperspectral ...
Index Terms-Homotopy, hyperspectral data classification, remote sensing, sparse representation, 1 -minimization. ...
Effect of Parameter Selection on Classification Accuracy Classification accuracy of a sparse-based representation is dependent on two parameters, i.e., regularization λ and error tolerance . ...
doi:10.1109/tgrs.2011.2172617
fatcat:sg6xrpsxujfexlmumovqgtusp4
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
[article]
2013
arXiv
pre-print
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Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. ...
This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image ...
Paolo Gamba for kindly providing the ROSIS images of University of Pavia and Center of Pavia and Prof. Landgrebe for the AVIRIS data. Finally, we would also like to thank Dr. ...
arXiv:1308.1187v1
fatcat:4jnynr5e5fhm7dzi6qwy7mna6m
Learning Discriminative Sparse Representations for Hyperspectral Image Classification
2015
IEEE Journal on Selected Topics in Signal Processing
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In sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule-based classification may lack generalization performance. ...
Index Terms-Hyperspectral image classification, discriminative sparse representation (DSR), total variation (TV), dictionary learning, sparse multinomial logistic regression (SMLR). ...
Landgrebe for making the Airborne Visible/Infrared Imaging Spectrometer Indian Pines hyperspectral data set available to the community and Prof. P. ...
doi:10.1109/jstsp.2015.2423260
fatcat:5aq7hzxltzc3ddg5nydcbmd3nm
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