Background Learning Based on Target Suppression Constraint for Hyperspectral Target Detection Article Swipe

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Weiying Xie , Xin Zhang , Yunsong Li , Keyan Wang , Qian Du ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1109/jstars.2020.3024903

Hyperspectral target detection is critical in both military and civilian applications. However, it is a challenging task due to the complexity of background and the limited samples of target in hyperspectral images (HSIs). In this article, we propose a novel background learning model, called background learning based on target suppression constraint to characterize high-dimensional spectral vectors. Considering insufficient target samples, the model is trained only on the background spectral samples to accurately learn the background distribution. Then the discrepancy between the reconstructed and original HSIs are examined to spot the targets. To obtain a background training dataset, coarse detection is carried out. However, it is quite difficult to retrieve pure background data. Thus, a target suppression constraint is imposed to reduce the impact of suspected target samples on background reconstruction. Experiments on six real HSIs demonstrate that the proposed framework significantly outperforms the current state-of-the-art detection methods and yields higher detection accuracy and lower false alarm rate.

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Artificial Intelligence

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Hyperspectral imaging Artificial intelligence Computer science Constraint (computer-aided design) Pattern recognition (psychology) Constant false alarm rate Object detection False alarm Mathematics Geometry

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/jstars.2020.3024903
PDF: https://ieeexplore.ieee.org/ielx7/4609443/8994817/09200776.pdf
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Cited By: 85
References: 50
Related Works: 10
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DOI: https://doi.org/10.1109/jstars.2020.3024903

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Title: Background Learning Based on Target Suppression Constraint for Hyperspectral Target Detection

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Language: en

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Publication year: 2020

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Publication date: 2020-01-01

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Authors: Weiying Xie, Xin Zhang, Yunsong Li, Keyan Wang, Qian Du

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Landing page: https://doi.org/10.1109/jstars.2020.3024903

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Concepts: Hyperspectral imaging, Artificial intelligence, Computer science, Constraint (computer-aided design), Pattern recognition (psychology), Constant false alarm rate, Object detection, False alarm, Mathematics, Geometry

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Cited by: 85

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primary_location.raw_source_name	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
primary_location.landing_page_url	https://doi.org/10.1109/jstars.2020.3024903
publication_date	2020-01-01
publication_year	2020
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