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Hyper-Graph Regularized Kernel Subspace Clustering for Band Selection of Hyperspectral Image
IEEE Access, 2020 Band selection is an effective way to deal with the problem of the Hughes phenomenon and high computation complexity in hyperspectral image (HSI) processing. Based on the hypothesis that all the pixels are sampled from the union of subspaces, many robust
Meng Zeng +5 more
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Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection
Remote Sensing, 2022 Hyperspectral band selection is a commonly used technique to alleviate the curse of dimensionality. Recently, clustering-based methods have attracted much attention for their effectiveness in selecting informative and representative bands.
Wenguang Wang, Wenhong Wang, Hongfu Liu
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Contribution of band selection and fusion for hyperspectral classification [PDF]
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014 For some specific land cover classification problems, it may be interesting to design superspectral camera systems with reduced numbers of bands (∼ 20) and optimized band widths. This paper assesses the contribution of band selection and band fusion processes separately and jointly for dimensionality reduction.
Nesrine Chehata +2 more
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EXTRACTION OF OPTIMAL SPECTRAL BANDS USING HIERARCHICAL BAND MERGING OUT OF HYPERSPECTRAL DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land ...
A. Le Bris +3 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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A Structural Subspace Clustering Approach for Hyperspectral Band Selection [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Band selection, which removes irrelevant bands from hyperspectral images (HSIs) and keeps essential spectral information contained in a relatively few bands, allows huge savings in data storage, computation time, and imaging hardware. In this article, we propose a novel structural subspace clustering (STSC) method for hyperspectral band selection ...
Shaoguang Huang +2 more
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Hyperspectral Band Selection via Band Grouping and Adaptive Multi-Graph Constraint
Remote Sensing, 2022 Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining.
Mengbo You +5 more
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Hyperspectral Band Selection via Optimal Combination Strategy
Remote Sensing, 2022 Band selection is one of the main methods of reducing the number of dimensions in a hyperspectral image. Recently, various methods have been proposed to address this issue.
Shuying Li +3 more
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Optimal Clustering Framework for Hyperspectral Band Selection [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2018 Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal ...
Qi Wang 0009 +2 more
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Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection
IEEE Access, 2019 Band selection is an important preprocessing technique for hyperspectral images to select a band subset with representative information and low correlation. However, most methods focus on removing redundant components without loss of original information,
Xiaoyan Luo +3 more
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