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
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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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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
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
doaj +1 more source
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
doaj +1 more source
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
Learning-Based Optimization of Hyperspectral Band Selection for Classification
Remote Sensing, 2023 Hyperspectral sensors acquire spectral responses from objects with a large number of narrow spectral bands. The large volume of data may be costly in terms of storage and computational requirements.
Cemre Omer Ayna +3 more
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Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection [PDF]
IEEE Transactions on Cybernetics, 2022 Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking.
He Sun +6 more
openaire +4 more sources