Results 21 to 30 of about 3,482,171 (251)
Hyperspectral Image Band Selection Based on CNN Embedded GA (CNNeGA)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral images (HSIs) are a powerful source of reliable data in various remote sensing applications. But due to the large number of bands, HSI has information redundancy, and methods are often used to reduce the number of spectral bands.
Mohammad Esmaeili +4 more
doaj +1 more source
, 2020 With hundreds of spectral bands, the rise of the issue of dimensionality in the classification of hyperspectral images is usually inevitable. In this paper, a restrictive polymorphic ant colony algorithm (RPACA) based band selection algorithm (RPACA-BS ...
Wu, P +6 more
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Hyperspectral imaging-based prediction of soluble sugar content in Chinese chestnuts
Frontiers in Forests and Global Change, 2023 Soluble sugars are critical determinants of fruit quality and play a significant role in human nutrition. Chestnuts, rich in soluble sugars, derive their sweetness from them.
Jinhui Yang +3 more
doaj +1 more source
Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
Applied Sciences, 2022 Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even
Loganathan Agilandeeswari +4 more
doaj +1 more source
University Concert Band, University Symphonic Band, November 13, 1980
, 2019 Recorded during a live performance at Miller Auditorium, Western Michigan University, Kalamazoo, Michigan on November 13, 1980, 8:00 p.m., program no.
Western Michigan University. Concert Band
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Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection
Remote Sensing, 2019 In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube.
Wenqiang Zhang +2 more
doaj +1 more source
University Concert Band, University Symphonic Band, February 17, 1980
, 2018 Recorded during a live performance at Miller Auditorium, Western Michigan University, Kalamazoo, Michigan, February 17, 1980, program no. 205 of the Department of Music's 1979-1980 season.University Concert Band, Gregory Talford, conductor (1st-4th works)
Western Michigan University. Concert Band
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Journal of Imaging, 2020 Despite the numerous band selection (BS) algorithms reported in the field, most if not all have exhibited maximal accuracy when more spectral bands are utilized for classification.
Ruben Moya Torres +5 more
doaj +1 more source
University Concert Band, University Symphonic Band, December 16, 1979
, 2018 Recorded during a live performance at Miller Auditorium, Western Michigan University, Kalamazoo, Michigan, December 16, 1979, program no. 142 of the Department of Music's 1979-1980 season.1st-4th works: University Concert Band, Gregory Talford, conductor.
Western Michigan University. Concert Band,
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Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection (BS) identifies key bands from hyperspectral imagery (HSI) for specific downstream tasks, playing a pivotal role in practical applications.
Xiaodi Shang +4 more
doaj +1 more source