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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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Improved SR-SSIM Band Selection Method Based on Band Subspace Partition
Remote Sensing, 2023 Scholars have performed much research on reducing the redundancy of hyperspectral data. As a measure of the similarity between hyperspectral bands, structural similarity is used in band selection methods.
Tingrui Hu +3 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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Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR [PDF]
, 2023 With continuous improvements in oil production, the environmental problems caused by oil exploitation are becoming increasingly serious. Rapid and accurate estimation of soil petroleum hydrocarbon content is of great significance to the investigation and
Qigang Jiang, Zhilian Li, Pengfei Shi
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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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An improved cuckoo search-based adaptive band selection for hyperspectral image classification
European Journal of Remote Sensing, 2020 The information in hyperspectral images usually has a strong correlation, a large number of bands, which lead to the “curse of dimensionality”. So, band selection is usually used to address this issue. However, problems remain for band selection, such as
Shiwei Shao
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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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Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection
Remote Sensing, 2022 Hyperspectral band selection is a commonly used technique to alleviate the curse of dimensionality. Recently, clustering-based methods have attracted much attention for their effectiveness in selecting informative and representative bands.
Wenguang Wang, Wenhong Wang, Hongfu Liu
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Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data [PDF]
, 2009 Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation.
Iriondo, Pedro M. +4 more
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