Band Selection by Divergence Distance Based on Gaussian Mixture Model for Hyperspectral Image Classification

Lahlimi, Mohammed

doi:10.30534/ijatcse/2019/72852019

Cited by 2 publications

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“…The divergence distance (DD) [19] is a probabilistic distance that measure of the similarity between two classes ω 1 and ω 2 often used in information theory. DD is the sum of the two Kullback-Leibler divergences.…”

Section: Divergence Distancementioning

confidence: 99%

“…A new band selection approach is introduced in this paper, based on the Kolmogorov Variational Distance for Hyperspectral image classification. This work is a sequel on our previous research on band selection with Mutual Information [18], Bhattacharyya Distance [8] and Divergence Distance [19]. The primary interest in KoVD is the fact that is uniquely related to the classification error [20] [6], which is often difficult to estimate [17].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Band Selection Approach for Hyperspectral Image Classification using the Kolmogorov Variational Distance

Lahlimi¹,

Kerroum²,

Fakhri³

2020

IJACSA

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In this paper, we introduce a novel band selection approach based on the Kolmogorov Variational Distance (KoVD) for Hyperspectral image classification. The main reason we are taking interest in KoVD is its unique relation to the classification error. Our previous works on band selection using the Mutual Information (MI), the Divergence Distance (DD), or the Bhattacharyya Distance (BD) inspire this study; thus, we are particularly interested in finding out how KoVD performs against these distances in terms of the numbers of band retained and the classification accuracy. All the distances in this study are modeled with the Gaussian Mixture Model (GMM) using the Bayes Information Criterion (BIC) / Robust Expectation-Maximization (REM). The experiments are carried on four benchmark Hyperspectral images: Kennedy Space Center, Salinas, Botswana, and Indian Pines (92AV3C). The results show that band selection based on the Kolmogorov Variational Distance performs better than BD and DD, meanwhile against MI the results were too close.

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Section: Divergence Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Band Selection Approach for Hyperspectral Image Classification using the Kolmogorov Variational Distance

Lahlimi¹,

Kerroum²,

Fakhri³

2020

IJACSA

Self Cite

View full text Add to dashboard Cite

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“…However, due to the increased number of spectral bands, more processing time is required for analyzing such images. Therefore, dimension reduction of HSI by selecting only the significant bands, without compromising the information content, has been an active area of research [1], [2]. In literature, many criteria such as-divergence, Bhattacharya distance, entropy has been used for the selection of bands that are crucial and significant in terms of information conservation [3], [4].…”

Section: Introductionmentioning

confidence: 99%

A Superpixel and Mutual Information Based Band Selection Method for Hyperspectral Images

Sarmah¹

2020

IJATCSE

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Dimension reduction is an important task in hyperspectral image (HSI) processing to minimize computational complexity. This work presents an efficient band selection method to select a set of highly relevant yet non-redundant bands using class-band mutual information (MI) and clustering technique.In HSI, the bands comprise continuous reflectance values, while the class labels in the ground truth are discrete. Thus before calculating class-band MI, the continuous reflectance values are converted to several discrete values using a novel graph-based superpixel generation method based on the spectral angle divergence (SAD). To ensure non-redundancy of the selected bands, clustering is performed to using correlation distance and then from each cluster only one representative band is selected using the calculated class-band MI. Experiments are carried out over three hyperspectral images to evaluate the efficacy of the proposed band selection technique. For each image, classification is performed using Support Vector Machine (SVM) classifier over both the compressed image with only the selected bands and the original image. The comparison results reveal the capability of the proposed method to maintain the classification accuracy with a substantially compact set of selected bands.

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Band Selection by Divergence Distance Based on Gaussian Mixture Model for Hyperspectral Image Classification

Cited by 2 publications

References 0 publications

A Novel Band Selection Approach for Hyperspectral Image Classification using the Kolmogorov Variational Distance

A Novel Band Selection Approach for Hyperspectral Image Classification using the Kolmogorov Variational Distance

A Superpixel and Mutual Information Based Band Selection Method for Hyperspectral Images

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