Optimal Clustering Framework for Hyperspectral Band Selection
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
Li, Xuelong, Wang, Qi, Zhang, Fahong
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Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings. [PDF]
Sensors (Basel), 2020 Gu X, Yang S, Liu Y, Hao R, Liu Z.
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Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery
Remote Sensing, 2022 Band selection (BS) is an effective pre-processing way to reduce the redundancy of hyperspectral data. Specifically, the band prioritization (BP) criterion plays an essential role since it can judge the importance of bands from a particular perspective ...
Xudong Sun +3 more
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Improved SR-SSIM Band Selection Method Based on Band Subspace Partition
Remote Sensing, 2023 Scholars have performed much research on reducing the redundancy of hyperspectral data. As a measure of the similarity between hyperspectral bands, structural similarity is used in band selection methods.
Tingrui Hu +3 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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Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection [PDF]
IEEE Transactions on Cybernetics, 2022 Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking.
He Sun +6 more
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Learning-Based Optimization of Hyperspectral Band Selection for Classification
Remote Sensing, 2023 Hyperspectral sensors acquire spectral responses from objects with a large number of narrow spectral bands. The large volume of data may be costly in terms of storage and computational requirements.
Cemre Omer Ayna +3 more
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HYBASE: hyperspectral band selection [PDF]
SPIE Proceedings, 2009 Band selection is essential in the design of multispectral sensor systems. This paper describes the TNO hyperspectral band selection tool HYBASE. It calculates the optimum band positions given the number of bands and the width of the spectral bands.
Schwering, P.B.W. +2 more
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Dual Homogeneous Patches-Based Band Selection Methodology for Hyperspectral Classification
Remote Sensing, 2023 Homogeneous band- or pixel-based feature selection, which exploits the difference between spectral or spatial regions to select informative and low-redundant bands, has been extensively studied in classifying hyperspectral images (HSIs).
Xianyue Wang +3 more
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Progressive band selection [PDF]
SPIE Proceedings, 2009 Progressive band selection (PBS) reduces spectral redundancy without significant loss of information, thereby reducing hyperspectral image data volume and processing time. Used onboard a spacecraft, it can also reduce image downlink time. PBS prioritizes an image's spectral bands according to priority scores that measure their significance to a ...
Kevin Fisher, Chein-I Chang
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