EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection [PDF]
Remote Sensing, 2021 Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing.
Yufei Liu +3 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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An Enhanced Jaya Algorithm with Mutation and Diversity-Preserving Strategies for Hyperspectral Band Selection [version 2; peer review: 2 approved] [PDF]
F1000ResearchHyperspectral band selection has become a key focus in hyperspectral image processing as it reduces the spectral redundancy and computational overhead, thereby improving classification performance.
Partha Pratim Sarangi +2 more
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Attend in Bands: Hyperspectral Band Weighting and Selection for Image Classification [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019 Hyperspectral remote sensing sensors have the ability to capture a wide range of spectrum of ground objects with hundreds to thousands of bands. The obtained hyperspectral images contain more detailed spectral information than conventional panchromatic or color images.
Jing Wang 0062 +2 more
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Reconstruction-Assisted Band Selection for Non-Destructive Prediction of Citrus Soluble Solids Content from VNIR Hyperspectral Images [PDF]
FoodsThe increasing demand for better fruit flavor and eating quality has driven the need for rapid and non-destructive assessment of internal attributes to support fruit grading and precision supply.
Junjie Zhao +6 more
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Hyperspectral Band Selection Method Based on Global Partition Clustering [PDF]
Remote SensingBand selection is an important step in the dimensionality reduction processing of hyperspectral images and is highly important for eliminating redundant spectral information and reducing computational costs.
Tingrui Hu, Xian Guo, Peichao Gao
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Hyperspectral Band Selection Using Improved Classification Map [PDF]
IEEE Geoscience and Remote Sensing Letters, 2017 Although it is a powerful feature selection algorithm, the wrapper method is rarely used for hyperspectral band selection. Its accuracy is restricted by the number of labeled training samples and collecting such label information for hyperspectral image is time consuming and expensive.
Xianghai Cao +3 more
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Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost [PDF]
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
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Similarity-Based Hyperspectral Band Selection Using Deep Reinforcement Learning [PDF]
, 2022 The main goal of hyperspectral band selection is to select a subset of bands to reduce the redundancy in hyperspectral images. Deep reinforcement learning was recently introduced for this task, which adopts a deep Q-network as the agent and information ...
Tuxworth, Gervase, Zhou, Jun, Bao, Dong
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Background-Aware Band Selection for Object Tracking in Hyperspectral Videos [PDF]
, 2023 Hyperspectral images contain many bands that can be used to obtain object material information for object tracking and remote sensing. Nevertheless, neighboring bands of hyperspectral images are often highly correlated, and a large number of bands ...
Islam, MA, Zhang, W, Gao, Y, Zhou, J
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