Sparse Principal Component Analysis and Adaptive Multigraph Learning for Hyperspectral Band Selection [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection (BS) is an effective dimensionality reduction technique for hyperspectral images. Although many relevant methods have been proposed, they often only focus on the bandwise information and the correlation between the bands, and few of them ...
Wenxian Zhang +3 more
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A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images [PDF]
, 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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Sparsity Regularized Deep Subspace Clustering for Multicriterion-Based Hyperspectral Band Selection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral images provide rich spectral information corresponding to visible and near-infrared imaging regions, facilitating accurate classification, object identification, and target detection. However, the high volume of data creates a computational
Samiran Das +3 more
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Constrained-Target Band Selection With Subspace Partition for Hyperspectral Target Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral target detection is widely used in both military and civilian fields. In practical applications, how to select a low-correlation and representative band subset to reduce redundancy is worth discussing.
Xudong Sun +4 more
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Efficient Graph Convolutional Self-Representation for Band Selection of Hyperspectral Image
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral image (HSI) band selection (BS) is an important task for HSI dimensionality reduction, whose goal is to select an informative band subset containing less redundancy.
Yaoming Cai +3 more
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Non-Parametric Spatial Spectral Band Selection methods [PDF]
, 2021 © Cranfield University 2021. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis project is about the development of band selection (BS) techniques for better target detection ...
Torres, Ruben M
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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
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Unsupervised hyperspectral band selection in the compressive sensing domain [PDF]
, 2019 Band selection (BS) algorithms are an effective means of reducing the high volume of redundant data produced by the hundreds of contiguous spectral bands of Hyperspectral images (HSI).
Chang, Chein-I +4 more
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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
Unsupervised Rate Distortion Function-Based Band Subset Selection for Hyperspectral Image Classification [PDF]
, 2023 Due to significant interband correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is one of the most widely used methods to reduce data dimensionality for band redundancy removal.
Chang, Chein-I +2 more
core +1 more source