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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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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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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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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Unsupervised Hyperspectral Band Selection using Clustering and Single-Layer Neural Network
Revue Française de Photogrammétrie et de Télédétection, 2018 Hyperspectral images provide rich spectral details of the observed scene by exploiting contiguous bands. But, the processing of such images becomes heavy, due to the high dimensionality.
Mateus Habermann +2 more
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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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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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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
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