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Robust classification of hyperspectral images
2007
Image and Signal Processing for Remote Sensing XIII
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This paper discusses robust classification of hyperspectral images. Both methods for dimensionality reduction and robust estimation of classifier parameters in full dimension are presented. ...
Two recently proposed techniques for covariance estimation based on the eigenvector decomposition and the Cholesky decomposition are compared to Support Vector Machine classifiers, simple regularized estimates ...
mixtures of Gaussians. ...
doi:10.1117/12.753095
fatcat:ubtcayt2tngxlp6iys46golqly
Band Selection by Divergence Distance Based on Gaussian Mixture Model for Hyperspectral Image Classification
2019
International Journal of Advanced Trends in Computer Science and Engineering
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In this work, we investigate a new band selection approach by Divergence distance based on the Gaussian Mixture Model (GMM) for Hyperspectral image classification. ...
Gaussian statistics of multivariate data. ...
Gaussian Mixture Model The Gaussian Mixture Model (GMM) captures non-Gaussian statistic of multivariate data [16] . ...
doi:10.30534/ijatcse/2019/72852019
fatcat:d7p6chgnf5bcdectsbxezkxy7q
Sparse Inverse Covariance Estimates for Hyperspectral Image Classification
2007
IEEE Transactions on Geoscience and Remote Sensing
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Classification of remotely sensed hyperspectral images calls for a classifier that gracefully handles high-dimensional data, where the amount of samples available for training might be very low relative ...
The resulting classifier is used on four different hyperspectral images, and compared with conventional approaches such as support vector machines, with encouraging results. ...
Storvik for valuable comments and suggestions. ...
doi:10.1109/tgrs.2007.892598
fatcat:msdidc55ebctlk7l3x2yvi2jrq
Parsimonious Gaussian Process Models for the Classification of Hyperspectral Remote Sensing Images
2015
IEEE Geoscience and Remote Sensing Letters
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Experimental results are given for three real hyperspectral data sets, and comparisons are done with three others classifiers. ...
A family of parsimonious Gaussian process models for classification is proposed in this letter. A subspace assumption is used to build these models in the kernel feature space. ...
The proposed models have been compared in terms of classification accuracy and processing time with three other classifiers, on three real hyperspectral data sets. ...
doi:10.1109/lgrs.2015.2481321
fatcat:s236axsgxvgltmfmlv7kiuhwdi
Bayesian Gaussian Mixture Model For Spatial-Spectral Classification Of Hyperspectral Images
2015
Zenodo
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For a -dimensional data, there are + ( 2 − )/2 number of unknowns to be estimated for a single mixture component ( unknowns for mean vector and ( 2 − )/2 unknowns for covariance matrix). ...
We propose to use a Bayesian Gaussian mixture model (GMM) for classification of HSI. ...
doi:10.5281/zenodo.38932
fatcat:ifzhck3pxrejplp5ydnh67mnoe
A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images
2007
IEEE Transactions on Geoscience and Remote Sensing
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For the latter, we properly define an easily scalable multiclass architecture capable to deal with incomplete training data. ...
Experimental results, obtained on different kinds of data (synthetic, hyperspectral, and multisensor images), point out the effectiveness of the SVDD technique and provide important indications for driving ...
Gamba (University of Pavia, Italy) for providing the reference data for the Pavia dataset, Prof. D. Landgrebe (Purdue University, USA) for providing the AVIRIS data, Dr. C.-J. ...
doi:10.1109/tgrs.2007.897425
fatcat:v5b637pomjh2zglr5tz3i4seki
A Novel Band Selection Approach for Hyperspectral Image Classification using the Kolmogorov Variational Distance
2020
International Journal of Advanced Computer Science and Applications
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All the distances in this study are modeled with the Gaussian Mixture Model (GMM) using the Bayes Information Criterion (BIC) / Robust Expectation-Maximization (REM). ...
In this paper, we introduce a novel band selection approach based on the Kolmogorov Variational Distance (KoVD) for Hyperspectral image classification. ...
For Gaussian Mixture Model, the curse of dimentionality is primarily related to the estimation of the covariance matrix [36] , and regularization techniques are one way around this problem:
1) Leave ...
doi:10.14569/ijacsa.2020.0111192
fatcat:l7i57d6lzbbp7bt632cighwa4q
Machine learning based hyperspectral image analysis: A survey
[article]
2019
arXiv
pre-print
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The image analysis tasks considered are land cover classification, target detection, unmixing, and physical parameter estimation. ...
The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian ...
The observation angle dependent (OAD) function is a covariance function designed primarily for classifying minerals from hyperspectral data with GPs [214, 270] . ...
arXiv:1802.08701v2
fatcat:bfi6qkpx2bf6bowhyloj2duugu
The Performance of Classifiers in the Task of Thematic Processing of Hyperspectral Images
2016
Journal of Siberian Federal University Engineering & Technologies
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The performance of the spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. ...
The results of classification of hyperspectral airborne images by using the specified above methods and comparative analysis are demonstrated. ...
Developments of Priority Directions in Science and Technology Complex of Russia on 2014-2020" (Grant Agreement No. 14.575.21.0028, its unique identification number RFMEFI57514X0028), the Russian Fund for ...
doi:10.17516/1999-494x-2016-9-7-1001-1011
fatcat:ntmfk7qahbb2xa42ijensj7f7q
A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction
2002
IEEE Transactions on Geoscience and Remote Sensing
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The simulated and real data results show that using NWFE then the mixture classifier based on nearest mean clustering and BIC_Mix index is a robust classification procedure for hyperspectral data. ...
In this paper, Mixed-LOOC2 is used with the parameter estimation and model selection steps of mixture classifiers. ...
doi:10.1109/tgrs.2002.805088
fatcat:wnxmulcdlfedrnxji4iw5cu3hm
A model-based mixture-supervised classification approach in hyperspectral data analysis
2002
IEEE Transactions on Geoscience and Remote Sensing
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In hyperspectral data analysis, usually classes of interest contain one or more components and may not be well represented by a single Gaussian density function. ...
In this paper, a model based mixture classifier, which uses mixture models to characterize class densities, is discussed. ...
Therefore the success of LOOL based estimators would be very limited in mixture classifiers unless statistics estimation is accompanied with EM. ...
doi:10.1109/tgrs.2002.807010
fatcat:eetqclrinrh5nm63co5rawyccy
Segmented Mixture-of-Gaussian Classification for Hyperspectral Image Analysis
2014
IEEE Geoscience and Remote Sensing Letters
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The locality-preserving discriminant analysis preserves the potentially multimodal statistical structure of the data, which the Gaussian mixture model classifier learns in the reduced-dimensional subspace ...
Subsequently, dimensionality reduction based on a graph-theoretic localitypreserving discriminant analysis is combined with classification driven by Gaussian mixture models independently in each subspace ...
Conclusion In this letter, we proposed a classification algorithm that couples spectral segmentation and mixture-of-Gaussian classification for hyperspectral image analysis-by exploiting the statistical ...
doi:10.1109/lgrs.2013.2250902
fatcat:riqe626wcjbw7a5rtu5mi2nqa4
Large-Scale Feature Selection With Gaussian Mixture Models for the Classification of High Dimensional Remote Sensing Images
2017
IEEE Transactions on Computational Imaging
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Index Terms-remote sensing, hyperspectral imaging, feature selection, gaussian mixture model, fast computing. ...
An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and block matrix. ...
For the GMM classifier with ridge, the selection of regularization parameter is more costly with more samples because of the classification rate estimation needed. ...
doi:10.1109/tci.2017.2666551
fatcat:qbuemk2pgna5jjgqm7s65x77ki
Unsupervised classification of hyperspectral images by using linear unmixing algorithm
2009
2009 16th IEEE International Conference on Image Processing (ICIP)
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In this paper, we present an unsupervised classification algorithm for hyperspectral images. ...
For reducing the dimension of hyperspectral data, we use a linear unmixing algorithm to extract the endmembers and their abundance maps. ...
Paulo Gamba, University of Pavia, for providing the data and the ground truth of the classification. ...
doi:10.1109/icip.2009.5413491
dblp:conf/icip/LuoC09a
fatcat:e7fro5vpobgszhq4fzrkkcwka4
Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields
2014
IEEE Geoscience and Remote Sensing Letters
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The Gaussian mixture model is a well-known classification tool that captures non-Gaussian statistics of multivariate data. ...
However, the impractically large size of the resulting parameter space has hindered widespread adoption of Gaussian mixture models for hyperspectral imagery. ...
Gamba for providing the ROSIS data and J. Li for sharing MATLAB code and helpful comments. ...
doi:10.1109/lgrs.2013.2250905
fatcat:4tc7tlwbezeo7go5hyi7fx2e2u
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