Results 11 to 20 of about 4,953 (252)
Mixed-Noise Band Selection for Hyperspectral Images [PDF]
IEEE Access, 2020 Hyperspectral images (HSIs) with abundant spectral information are generally susceptible to various types of noise, such as Gaussian noise and stripe noise.
Zhen Li, Chenwei Deng, Yun Huang
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Representative Band Selection for Hyperspectral Image Classification [PDF]
ISPRS International Journal of Geo-Information, 2018 The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while
Fuding Xie +3 more
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Nonlocal Band Attention Network for Hyperspectral Image Band Selection [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Band selection (BS) is a foundational problem for the analysis of high-dimensional hyperspectral image (HSI) cubes. Recent developments in the visual attention mechanism allow for specifically modeling the complex relationship among different components.
Tiancong Li +4 more
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Band Subset Selection for Hyperspectral Image Classification [PDF]
Remote Sensing, 2018 This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one ...
Chunyan Yu, Meiping Song, Chein-I Chang
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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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Problem-based band selection for hyperspectral images [PDF]
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017 This paper addresses the band selection of a hyperspectral image. Considering a binary classification, we devise a method to choose the more discriminating bands for the separation of the two classes involved, by using a simple algorithm: single-layer neural network. After that, the most discriminative bands are selected, and the resulting reduced data
Habermann, Mateus +2 more
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A Split-and-Merge Approach for Hyperspectral Band Selection [PDF]
IEEE Geoscience and Remote Sensing Letters, 2017 The problem of band selection (BS) is of great importance to handle the curse of dimensionality for hyperspectral image (HSI) applications (e.g., classification). This letter proposes an unsupervised BS approach based on a split-and-merge concept. This new approach provides relevant spectral sub-bands by splitting the adjacent bands without violating ...
Shaheera Rashwan, Nicolas Dobigeon
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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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Hyper-Graph Regularized Kernel Subspace Clustering for Band Selection of Hyperspectral Image
IEEE Access, 2020 Band selection is an effective way to deal with the problem of the Hughes phenomenon and high computation complexity in hyperspectral image (HSI) processing. Based on the hypothesis that all the pixels are sampled from the union of subspaces, many robust
Meng Zeng +5 more
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Remote Sensing, 2022 It has been widely certified that hyperspectral images can be effectively used to monitor soil organic matter (SOM). Though numerous bands reveal more details in spectral features, information redundancy and noise interference also come accordingly.
Linya Zhao +6 more
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