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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BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation [PDF]
SensorsHyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency.
Mohammad Rahman +4 more
doaj +2 more sources
Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module [PDF]
Sensors, 2023 Corn diseases are one of the significant constraints to high–quality corn production, and accurate identification of corn diseases is of great importance for precise disease control.
Yinjiang Jia +3 more
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Band Selection for Dehazing Algorithms Applied to Hyperspectral Images in the Visible Range [PDF]
Sensors, 2021 Images captured under bad weather conditions (e.g., fog, haze, mist, dust, etc.), suffer from poor contrast and visibility, and color distortions. The severity of this degradation depends on the distance, the density of the atmospheric particles and the ...
Sol Fernández-Carvelo +5 more
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Bathymetric-Based Band Selection Method for Hyperspectral Underwater Target Detection [PDF]
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
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Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification [PDF]
Remote SensingBand clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue.
Chein-I Chang +2 more
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Saliency computation via whitened frequency band selection. [PDF]
Cogn Neurodyn, 2016 Many saliency computational models have been proposed to simulate bottom-up visual attention mechanism of human visual system. However, most of them only deal with certain kinds of images or aim at specific applications. In fact, human beings have the ability to correctly select attentive focuses of objects with arbitrary sizes within any scenes.
Lv Q, Wang B, Zhang L.
europepmc +4 more sources
Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection. [PDF]
Sensors (Basel), 2023 Li Z, Wang W.
europepmc +3 more sources
Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection. [PDF]
Sensors (Basel), 2021 Maliuk AS +4 more
europepmc +3 more sources
Interband Consistency-Driven Structural Subspace Clustering for Unsupervised Hyperspectral Band Selection. [PDF]
Sensors (Basel)Wang Z, Wang W.
europepmc +3 more sources