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Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery

The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line proces...

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Detalles Bibliográficos
Autores principales: Zhao, Chunhui, Deng, Weiwei, Yan, Yiming, Yao, Xifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579979/
https://www.ncbi.nlm.nih.gov/pubmed/28783125
http://dx.doi.org/10.3390/s17081815
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author Zhao, Chunhui
Deng, Weiwei
Yan, Yiming
Yao, Xifeng
author_facet Zhao, Chunhui
Deng, Weiwei
Yan, Yiming
Yao, Xifeng
author_sort Zhao, Chunhui
collection PubMed
description The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line processing of KRXD (PLP-KRXD) that can perform KRXD line by line (the main data acquisition pattern). Parallel causal sliding windows are defined to ensure the causality of PLP-KRXD. Then, with the employment of the Woodbury matrix identity and the matrix inversion lemma, PLP-KRXD has the capacity to recursively update the kernel matrices, thereby avoiding a great many repetitive calculations of complex matrices, and greatly reducing the algorithm’s complexity. To substantiate the usefulness and effectiveness of PLP-KRXD, three groups of hyperspectral datasets are used to conduct experiments.
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spelling pubmed-55799792017-09-06 Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery Zhao, Chunhui Deng, Weiwei Yan, Yiming Yao, Xifeng Sensors (Basel) Article The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line processing of KRXD (PLP-KRXD) that can perform KRXD line by line (the main data acquisition pattern). Parallel causal sliding windows are defined to ensure the causality of PLP-KRXD. Then, with the employment of the Woodbury matrix identity and the matrix inversion lemma, PLP-KRXD has the capacity to recursively update the kernel matrices, thereby avoiding a great many repetitive calculations of complex matrices, and greatly reducing the algorithm’s complexity. To substantiate the usefulness and effectiveness of PLP-KRXD, three groups of hyperspectral datasets are used to conduct experiments. MDPI 2017-08-07 /pmc/articles/PMC5579979/ /pubmed/28783125 http://dx.doi.org/10.3390/s17081815 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Chunhui
Deng, Weiwei
Yan, Yiming
Yao, Xifeng
Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
title Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
title_full Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
title_fullStr Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
title_full_unstemmed Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
title_short Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
title_sort progressive line processing of kernel rx anomaly detection algorithm for hyperspectral imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579979/
https://www.ncbi.nlm.nih.gov/pubmed/28783125
http://dx.doi.org/10.3390/s17081815
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