Results 11 to 20 of about 9,721 (245)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015 Curse of dimensionality is a major disadvantage for classification of hyperspectral imagery since a large number of bands need to be dealt with. Band selection is a task to reduce the number of bands. An unsupervised band selection method is proposed in this article. It is a three-step procedure.
Aloke Datta +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
doaj +1 more source
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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Segmented Autoencoders for Unsupervised Embedded Hyperspectral Band Selection [PDF]
2018 7th European Workshop on Visual Information Processing (EUVIP), 2018 One of the major challenges in hyperspectral imaging (HSI) is the selection of the most informative wavelengths within the vast amount of data in a hypercube. Band selection can reduce the amount of data and computational cost as well as counteracting the negative effects of redundant and erroneous information. In this paper, we propose an unsupervised,
Tschannerl, Julius +3 more
openaire +2 more sources
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
doaj +1 more source
Unsupervised hyperspectral band selection using parallel processing [PDF]
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009 Band selection is a common technique to reducing the data dimensionality of hyperspectral imagery. When the desired object information is unknown, the objective of an unsupervised band selection approach is to select the most distinctive and informative bands. Although band selection can significantly alleviate the computational burden in the following
He Yang, Qian Du 0001
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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
doaj +1 more source
Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection
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
doaj +1 more source
EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection
Remote Sensing, 2021 Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing.
Yufei Liu +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
doaj +1 more source