Band Priority Index: A Feature Selection Framework for Hyperspectral Imagery [PDF]
Remote Sensing, 2018 Hyperspectral Band Selection (BS) aims to select a few informative and distinctive bands to represent the whole image cube. In this paper, an unsupervised BS framework named the band priority index (BPI) is proposed.
Wenqiang Zhang +2 more
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An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images [PDF]
Remote Sensing, 2023 Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs).
Xiaorun Li +3 more
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Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications [PDF]
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
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Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image [PDF]
IET Computer Vision, 2019 Hyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral
Xuefeng Jiang +3 more
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Double Deep Q-Network for Hyperspectral Image Band Selection in Land Cover Classification Applications [PDF]
Remote Sensing, 2023 Hyperspectral data usually consists of hundreds of narrow spectral bands and provides more detailed spectral characteristics compared to commonly used multispectral data in remote sensing applications.
Hua Yang +5 more
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Hyperspectral Band Selection via Heterogeneous Graph Convolutional Self-Representation Network [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain,
Junde Chen +3 more
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Anomaly‐background separation and particle swarm optimization based band selection for hyperspectral anomaly detection [PDF]
IET Image ProcessingAs one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface objects ...
Xiaodi Shang +4 more
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MOBS-TD: Multiobjective Band Selection With Ideal Solution Optimization Strategy for Hyperspectral Target Detection [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection (BS) is a crucial concept within the realm of remote sensing, involving the selection of the most suitable bands to accurately capture features of landforms and surfaces.
Xudong Sun +4 more
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Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images [PDF]
Remote Sensing, 2017 Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each
Chen Yang +4 more
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Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images [PDF]
Remote Sensing, 2017 This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD
Sicong Liu +5 more
doaj +2 more sources