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Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification
Remote Sensing, 2022 A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes ...
Mateus Habermann +2 more
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European Journal of Remote Sensing, 2019 Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection techniques. These methods are the most popular ways for dimensionality reduction of original data.
Sarra Ikram Benabadji +5 more
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Two-Stage Unsupervised Hyperspectral Band Selection Based on Deep Reinforcement Learning
Remote SensingHyperspectral images are high-dimensional data that capture detailed spectral information across a wide range of wavelengths, enabling the precise identification and analysis of different materials or objects. However, the high dimensionality of the data
Yi Guo +4 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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Sensors, 2023 Unsupervised band selection is an essential task to search for representative bands in hyperspectral dimension reduction. Most of existing studies utilize the inherent attribute of hyperspectral image (HSI) and acquire single optimal band subset while ...
Yunpeng Wei +3 more
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Engineering Proceedings, 2023 Band selection is a frequently used dimension reduction technique for hyperspectral images (HSI) to address the “curse of dimensionality” phenomenon in machine learning (ML). This technique identifies and selects a subset of the most important bands from
Sara Benali +2 more
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Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection [PDF]
Remote Sensing, 2020 Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated.
Peifeng Su +2 more
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Bathymetric-Based Band Selection Method for Hyperspectral Underwater Target Detection
Remote Sensing, 2021 Band selection has imposed great impacts on hyperspectral image processing in recent years. Unfortunately, few existing methods are proposed for hyperspectral underwater target detection (HUTD).
Jiahao Qi +6 more
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An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images
Remote Sensing, 2023 Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs).
Xiaorun Li +3 more
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Robust Unsupervised Hyperspectral Band Selection via Global Affinity Matrix Reconstruction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Unsupervised band selection is fundamental to alleviate the curse of dimensionality for hyperspectral imagery. Although many research works have been developed, it is still a challenging problem to improve the poor classification performance with a small
Mengbo You +3 more
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