Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning

Borges, Janete S.; Bioucas-Dias, José M.; Marçal, A.

doi:10.1109/tgrs.2010.2097268

Cited by 59 publications

(15 citation statements)

References 114 publications

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“…Pixels are no longer processed individually but the intrinsic 3D nature of the hyperspectral data cube is capitalized by taking advantage of the correlations between spatial and spectral neighbors (see, e.g. [191]- [198].…”

Section: Spatial-spectral Contextual Informationmentioning

confidence: 99%

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Bioucas-Dias

Plaza

Dobigeon

et al. 2012

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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2,343

544

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International audienceImaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally

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Section: Spatial-spectral Contextual Informationmentioning

confidence: 99%

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Bioucas-Dias

Plaza

Dobigeon

et al. 2012

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

2,343

544

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“…One of the MRF models is the classic maximum a posteriori Markov random filed (MAP-MRF) framework, which was first proposed in 1984. It employs Bayesian statistical guidelines to analyze the machine vision problem [43]. Another method considered in the MRF model is graph cuts (GC) [44,45].…”

Section: Introductionmentioning

confidence: 99%

Nearest-Regularized Subspace Classification for PolSAR Imagery Using Polarimetric Feature Vector and Spatial Information

Zhang

Yin

et al. 2017

Remote Sensing

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Feature extraction using polarimetric synthetic aperture radar (PolSAR) images is of great interest in SAR classification, no matter if it is applied in an unsupervised approach or a supervised approach. In the supervised classification framework, a major group of methods is based on machine learning. Various machine learning methods have been investigated for PolSAR image classification, including neural network (NN), support vector machine (SVM), and so on. Recently, representation-based classifications have gained increasing attention in hyperspectral imagery, such as the newly-proposed sparse-representation classification (SRC) and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic SVM for remotely-sensed image processing. However, rare studies have been found to extend this representation-based NRS classification into PolSAR images. By the use of the NRS approach, a polarimetric feature vector-based PolSAR image classification method is proposed in this paper. The polarimetric SAR feature vector is constructed by the components of different target decomposition algorithms for each pixel, including those scattering components of Freeman, Huynen, Krogager, Yamaguchi decomposition, as well as the eigenvalues, eigenvectors and their consequential parameters such as entropy, anisotropy and mean scattering angle. Furthermore, because all these representation-based methods were originally designed to be pixel-wise classifiers, which only consider the separate pixel signature while ignoring the spatial-contextual information, the Markov random field (MRF) model is also introduced in our scheme. MRF can provide a basis for modeling contextual constraints. Two AIRSAR data in the Flevoland area are used to validate the proposed classification scheme. Experimental results demonstrate that the proposed method can reach an accuracy of around 99% for both AIRSAR data by randomly selecting 300 pixels of each class as the training samples. Under the condition that the training data ratio is more than 4%, it has better performance than the SVM, SVM-MRF and NRS methods.

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“…Hidden Markov chains and random fields are used in [36] for radar image classification. [37] exploits a Potts-Markov model with MnL class densities in hyperspectral image segmentation. A double MRFs model is proposed in [23] for optical images to model the texture and the class labels as two different random fields.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Amplitude and Texture Classification of SAR Images With Multinomial Latent Model

Kayabol

Zerubia

2013

IEEE Trans. on Image Process.

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Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning

Cited by 59 publications

References 114 publications

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Nearest-Regularized Subspace Classification for PolSAR Imagery Using Polarimetric Feature Vector and Spatial Information

Unsupervised Amplitude and Texture Classification of SAR Images With Multinomial Latent Model

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