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A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest

Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usual...

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Autores principales: Jia, Yuewei, Xue, Lingyun, Xu, Ping, Luo, Bin, Chen, Ke-nan, Zhu, Lei, Liu, Yian, Yan, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641574/
https://www.ncbi.nlm.nih.gov/pubmed/34909466
http://dx.doi.org/10.7717/peerj-cs.802
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author Jia, Yuewei
Xue, Lingyun
Xu, Ping
Luo, Bin
Chen, Ke-nan
Zhu, Lei
Liu, Yian
Yan, Ming
author_facet Jia, Yuewei
Xue, Lingyun
Xu, Ping
Luo, Bin
Chen, Ke-nan
Zhu, Lei
Liu, Yian
Yan, Ming
author_sort Jia, Yuewei
collection PubMed
description Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usually does not contain useful information, which wastes a lot of storage resources. In this paper, a novel hyperspectral compressive sensing framework for plant leaves with MAROIs (HCSMAROI) is proposed to alleviate these problems. HCSMAROI only compresses and reconstructs MAROIs by discarding the background to achieve good reconstructed performance. But for different plant leaf HSIs, HCSMAROI has the potential to be applied in other HSIs. Firstly, spatial spectral decorrelation criterion (SSDC) is used to obtain the optimal band of plant leaf HSIs; Secondly, different leaf regions and background are distinguished by the mask image of the optimal band; Finally, in order to improve the compression efficiency, after discarding the background region the compressed sensing technology based on blocking and expansion is used to compress and reconstruct the MAROIs of plant leaves one by one. Experimental results of soybean leaves and tea leaves show that HCSMAROI can achieve 3.08 and 5.05 dB higher PSNR than those of blocking compressive sensing (BCS) at the sampling rate of 5%, respectively. The reconstructed spectra of HCSMAROI are especially closer to the original ones than that of BCS. Therefore, HCSMAROI can achieve significantly higher reconstructed performance than that of BCS. Moreover, HCSMAROI can provide a flexible way to compress and reconstruct different MAROIs with different sampling rates, while achieving good reconstruction performance in the spatial and spectral domains.
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spelling pubmed-86415742021-12-13 A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest Jia, Yuewei Xue, Lingyun Xu, Ping Luo, Bin Chen, Ke-nan Zhu, Lei Liu, Yian Yan, Ming PeerJ Comput Sci Algorithms and Analysis of Algorithms Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usually does not contain useful information, which wastes a lot of storage resources. In this paper, a novel hyperspectral compressive sensing framework for plant leaves with MAROIs (HCSMAROI) is proposed to alleviate these problems. HCSMAROI only compresses and reconstructs MAROIs by discarding the background to achieve good reconstructed performance. But for different plant leaf HSIs, HCSMAROI has the potential to be applied in other HSIs. Firstly, spatial spectral decorrelation criterion (SSDC) is used to obtain the optimal band of plant leaf HSIs; Secondly, different leaf regions and background are distinguished by the mask image of the optimal band; Finally, in order to improve the compression efficiency, after discarding the background region the compressed sensing technology based on blocking and expansion is used to compress and reconstruct the MAROIs of plant leaves one by one. Experimental results of soybean leaves and tea leaves show that HCSMAROI can achieve 3.08 and 5.05 dB higher PSNR than those of blocking compressive sensing (BCS) at the sampling rate of 5%, respectively. The reconstructed spectra of HCSMAROI are especially closer to the original ones than that of BCS. Therefore, HCSMAROI can achieve significantly higher reconstructed performance than that of BCS. Moreover, HCSMAROI can provide a flexible way to compress and reconstruct different MAROIs with different sampling rates, while achieving good reconstruction performance in the spatial and spectral domains. PeerJ Inc. 2021-11-25 /pmc/articles/PMC8641574/ /pubmed/34909466 http://dx.doi.org/10.7717/peerj-cs.802 Text en © 2021 Jia et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Jia, Yuewei
Xue, Lingyun
Xu, Ping
Luo, Bin
Chen, Ke-nan
Zhu, Lei
Liu, Yian
Yan, Ming
A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest
title A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest
title_full A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest
title_fullStr A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest
title_full_unstemmed A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest
title_short A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest
title_sort novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641574/
https://www.ncbi.nlm.nih.gov/pubmed/34909466
http://dx.doi.org/10.7717/peerj-cs.802
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