A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

Moan, Steven Le; Mansouri, Alamin; Voisin, Yvon; Hardeberg, Jon Yngve

doi:10.1109/tgrs.2011.2158319

Cited by 49 publications

(29 citation statements)

References 43 publications

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“…The process of band selection thus turns out to be the optimization problem [15], [18], [23]. In some works, the correlations between spectral bands can also be interpreted from the information theory, such as mutual information [24], [25] and dependent information [7]. As a limitation, these algorithms are mostly unsupervised approaches.…”

Section: B Band Selection Methodsmentioning

confidence: 99%

Semisupervised Pair-Wise Band Selection for Hyperspectral Images

Bai

Xiang

Shi

et al. 2015

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

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This paper proposes a new approach of band selection for classifying multiple objects in hyperspectral images. Different from traditional algorithms, we construct a semisupervised pair-wise band selection (PWBS) framework for this task, in which an individual band selection process is performed only for each pair of classes. First, the statistical parameters for spectral features of each class, including mean vectors and covariance matrices, are estimated by an expectation maximization approach in a semisupervised learning setting, where both labeled and unlabeled samples are employed for better performance. For each pair of classes, based on the estimated statistical parameters, Bhattacharyya distances between the two classes are calculated to evaluate all possible subsets of bands for classification. Second, as our proposed semisupervised framework, the PWBS followed by a binary classifier can be embedded into the semisupervised expectation maximization process to obtain posterior probabilities of samples on the selected bands. Finally, to evaluate the selected bands, all of the binary decisions obtained with multiple binary classifiers are finally fused together. Comparative experimental results demonstrate the validity of our proposed algorithm. The experimental results also prove that our band selection algorithm can perform well when the training set is very small.

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Section: B Band Selection Methodsmentioning

confidence: 99%

Semisupervised Pair-Wise Band Selection for Hyperspectral Images

Bai

Xiang

Shi

et al. 2015

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

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“…Due to this fact, researchers have been employing other approaches for the visualization of spectral data, e.g. by incorporating information measures [12].…”

Section: Processingmentioning

confidence: 99%

On the Quality Evaluation of Spectral Image Processing Algorithms

Deborah

Richard

Hardeberg

2014

2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems

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International audienceThe main contribution of this work lies in the context of the quality assessment of spectral image processing operators or algorithms. Several means to define quality measurements for low-level spectral image processing tools are explored, i.e. Spectral distance and spectral image filtering. The quality assessment starts from simple theoretical validation on simulated spectra to reduced and full reference quality assessment on real spectral data. Spectral image of a pigment patch with different shades and a simple model of spectral noise are proposed to be used in reduced and full reference quality assessment. Finally, given an application purpose, this quality assessment protocol will aid reader in selecting a correct spectral distance which will be the core of other distance-based spectral image processing tools

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“…We used the band selection approach that was used in [15] without the spectrum segmentation. It is based on a progressive research of dissimilar channels from single to third order.…”

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

Salient Pixels and Dimensionality Reduction for Display of Multi/Hyperspectral Images

Moan

Deger

Mansouri

et al. 2012

Lecture Notes in Computer Science

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Abstract. Dimensionality Reduction (DR) of spectral images is a common approach to different purposes such as visualization, noise removal or compression. Most methods such as PCA or band selection use either the entire population of pixels or a uniformly sampled subset in order to compute a projection matrix. By doing so, spatial information is not accurately handled and all the objects contained in the scene are given the same emphasis. Nonetheless, it is possible to focus the DR on the separation of specific Objects of Interest (OoI), simply by neglecting all the others. In PCA for instance, instead of using the variance of the scene in each spectral channel, we show that it is more efficient to consider the variance of a small group of pixels representing several OoI, which must be separated by the projection. We propose an efficient method based on saliency to automatically identify OoI and extract only a few relevant pixels to enhance the separation foreground/background in the DR process.

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A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

Cited by 49 publications

References 43 publications

Semisupervised Pair-Wise Band Selection for Hyperspectral Images

Semisupervised Pair-Wise Band Selection for Hyperspectral Images

On the Quality Evaluation of Spectral Image Processing Algorithms

Salient Pixels and Dimensionality Reduction for Display of Multi/Hyperspectral Images

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