EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection
Remote Sensing, 2021 Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing.
Yufei Liu +3 more
doaj +1 more source
A Local Potential-Based Clustering Algorithm for Unsupervised Hyperspectral Band Selection
IEEE Access, 2019 Unsupervised band selection plays an increasingly important role in a hyperspectral image (HSI) classification because of inadequate labeling samples.
Zhaokui Li +5 more
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Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012 The rich information available in hyperspectral imagery has provided significant opportunities for material classification and identification. Due to the problem of the “curse of dimensionality” (called Hughes phenomenon) posed by the high number of spectral channels along with small amounts of labeled training samples, dimensionality reduction is a ...
Sen Jia 0001 +3 more
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Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters [PDF]
, 2012 In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation.Instead of using model parameters as texture features, we exploit the variations ...
Mahmoodi, Sasan +3 more
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Multiple-Feature Kernel-Based Probabilistic Clustering for Unsupervised Band Selection [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2019 This paper presents a new method to perform unsupervised band selection (UBS) with hyperspectral data. The method provides a probabilistic clustering approach. The band images are clustered in the image space by computing their posterior class probability.
Marco Bevilacqua, Yannick Berthoumieu
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Improving Hyperspectral Pixel Classification With Unsupervised Training Data Selection [PDF]
, 2014 An unsupervised method for selecting training data is suggested here. The method is tested by applying it to hyperspectral land-use classification.
Robert P. W. Duin +6 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection is a critical step in processing hyperspectral imagery (HSI); reducing input dimensionality allows models to mitigate redundancy, enhance computational efficiency, and improve learning accuracy.
Jacqueline Liu +2 more
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral band selection is a critical step for reducing data dimensionality, suppressing spectral redundancy, and improving the efficiency of subsequent analysis.
Lin Hu +6 more
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Unsupervised Band Selection of Hyperspectral Images via Multi-Dictionary Sparse Representation
IEEE Access, 2018 Band selection is a direct and effective method to reduce the spectral dimension, which is one of popular topics in hyperspectral remote sensing. Recently, a number of methods were proposed to deal with the band selection problem.
Fei Li, Pingping Zhang, Lu Huchuan
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Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification [PDF]
, 2022 Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both.
Hua, Yuansheng +12 more
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