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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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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Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection [PDF]
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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Interband Consistency-Driven Structural Subspace Clustering for Unsupervised Hyperspectral Band Selection [PDF]
SensorsIn the classification applications of hyperspectral remote sensing images (HSIs), band selection is crucial for mitigating the curse of dimensionality while preserving the intrinsic physical information within HSIs.
Zengke Wang, Wenhong Wang
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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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Unsupervised Hyperspectral Band Selection Using Spectral–Spatial Iterative Greedy Algorithm [PDF]
SensorsHyperspectral band selection (BS) is an important technique to reduce data dimensionality for the classification applications of hyperspectral remote sensing images (HSIs). Recently, searching-based BS methods have received increasing attention for their
Xin Yang, Wenhong Wang
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Rapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donors: A Pilot Machine Learning Study [PDF]
MetabolitesBackground/Objectives: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transform infrared (FTIR) spectroscopy of static cold-storage Celsior® perfusion fluid can discriminate kidneys recovered
Luis Ramalhete +7 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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Unsupervised Hyperspectral Band Selection Using Graphics Processing Units
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011 The high dimensionality of hyperspectral imagery challenges image processing and analysis. Band selection is a common technique for dimensionality reduction. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands.
Qian Du
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Multiobjective Optimization-Based Hyperspectral Unsupervised Band Selection for Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection (BS) is a critical topic in hyperspectral image dimensionality reduction, focusing on identifying representative bands that can convey the essential information of the full bands without significant loss.
Shihui Liu +4 more
doaj +2 more sources