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J. Imaging
Special Issues
Hyperspectral Imaging and Its Applications
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Hyperspectral Imaging and Its Applications

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Color, Multi-spectral, and Hyperspectral Imaging".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 26709

Special Issue Editors

Prof. Dr. Juan Luis Nieves Gómez

E-Mail Website
Guest Editor
Department of Optics, University of Granada, 18071 Granada, Spain
Interests: color imaging; computational color; spectral imaging; color; color vision
Dr. Miguel A. Martínez-Domingo

E-Mail Website
Guest Editor
Department of Optics, University of Granada, 18071 Granada, Spain
Interests: spectral and color imaging; computational color; color vision; high dynamic range imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue covers spectral imaging, related technologies, and their applications in science, engineering, industry, vision, and cultural heritage. The goal of spectral imaging is to recover radiance and reflectance spectra at each pixel in a scene. Typically, such spectral imaging systems consist of a digital sensor coupled to a dispersive device, which divides the incoming light into several spectral bands. These spectral channels might be narrow or broad-band. Depending on the number of bands used, spectral systems can be classified into multispectral imaging systems, when a small number of 3–10 bands is used; hyperspectral systems, if the number of bands extends up to 10–100; or ultraspectral, when the number is greater than 100. If the number of bands is sufficiently large and their bandwidths are sufficiently small, as with hyperspectral and ultraspectral imaging systems, spectral data can be recovered with high accuracy and precision.

This Special Issue aims to highlight the advances in spectral imaging science, focusing on theoretical, applied, and technological applications. The contents include image capture procedures, the spectral characterization of image capture devices, the estimation of spectral functions from conventional image capture systems, evaluation of the accuracy or performance of multispectral images, computational spectral imaging, spectral image processing, registration, encoding, compression, polarimetric, high dynamic range or panoramic spectral imaging, spectral imaging with mobile devices, etc. Moreover, this Issue covers applications in agriculture, remote sensing, medicine, food analysis, mineralogy, surveillance, chemical imaging, forensic science, beauty care, image restoration, art, cultural heritage, and archeology.

Prof. Dr. Juan Luis Nieves Gómez
Dr. Miguel A. Martínez-Domingo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Spectral imaging
  • Multispectral
  • Hyperspectral
  • Color
  • Image capture
  • Image processing
  • Sensors

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Published Papers (5 papers)

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Research

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22 pages, 1451 KiB  
Article
Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
by Mohamed Benouis, Leandro D. Medus, Mohamed Saban, Abdessattar Ghemougui and Alfredo Rosado-Muñoz
J. Imaging 2021, 7(9), 186; https://doi.org/10.3390/jimaging7090186 - 16 Sep 2021
Cited by 9 | Viewed by 3426
Abstract
A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of [...] Read more.
A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Its Applications)
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13 pages, 4374 KiB  
Article
Recycling-Oriented Characterization of Post-Earthquake Building Waste by Different Sensing Techniques
by Oriana Trotta, Giuseppe Bonifazi, Giuseppe Capobianco and Silvia Serranti
J. Imaging 2021, 7(9), 182; https://doi.org/10.3390/jimaging7090182 - 8 Sep 2021
Cited by 14 | Viewed by 2528
Abstract
In this paper, a methodological approach based on hyperspectral imaging (HSI) working in the short-wave infrared range (1000–2500 nm) was developed and applied for the recycling-oriented characterization of post-earthquake building waste. In more detail, the presence of residual cement mortar on the surface [...] Read more.
In this paper, a methodological approach based on hyperspectral imaging (HSI) working in the short-wave infrared range (1000–2500 nm) was developed and applied for the recycling-oriented characterization of post-earthquake building waste. In more detail, the presence of residual cement mortar on the surface of tile fragments that can be recycled as aggregates was estimated. The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis (PCA) for data exploration and partial least-squares-discriminant analysis (PLS-DA) to build classification models. Micro-X-ray fluorescence (micro-XRF) maps were also obtained on the same samples in order to validate the HSI classification results. Results showed that it is possible to identify cement mortar on the surface of the recycled tile, evaluating its degree of liberation. The recognition is automatic and non-destructive and can be applied for recycling-oriented purposes at recycling plants. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Its Applications)
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16 pages, 2979 KiB  
Article
Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection
by Paola Cucuzza, Silvia Serranti, Giuseppe Bonifazi and Giuseppe Capobianco
J. Imaging 2021, 7(9), 181; https://doi.org/10.3390/jimaging7090181 - 8 Sep 2021
Cited by 15 | Viewed by 3542
Abstract
In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were [...] Read more.
In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000–2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Its Applications)
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13 pages, 7969 KiB  
Article
Low-Cost Hyperspectral Imaging with A Smartphone
by Mary B. Stuart, Andrew J. S. McGonigle, Matthew Davies, Matthew J. Hobbs, Nicholas A. Boone, Leigh R. Stanger, Chengxi Zhu, Tom D. Pering and Jon R. Willmott
J. Imaging 2021, 7(8), 136; https://doi.org/10.3390/jimaging7080136 - 5 Aug 2021
Cited by 30 | Viewed by 10084
Abstract
Recent advances in smartphone technologies have opened the door to the development of accessible, highly portable sensing tools capable of accurate and reliable data collection in a range of environmental settings. In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that [...] Read more.
Recent advances in smartphone technologies have opened the door to the development of accessible, highly portable sensing tools capable of accurate and reliable data collection in a range of environmental settings. In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that can convert a standard smartphone camera into a visible wavelength hyperspectral sensor for ca. £100. To the best of our knowledge, this represents the first smartphone capable of hyperspectral data collection without the need for extensive post processing. The Hyperspectral Smartphone’s abilities are tested in a variety of environmental applications and its capabilities directly compared to the laboratory-based analogue from our previous research, as well as the wider existing literature. The Hyperspectral Smartphone is capable of accurate, laboratory- and field-based hyperspectral data collection, demonstrating the significant promise of both this device and smartphone-based hyperspectral imaging as a whole. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Its Applications)
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Review

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21 pages, 6615 KiB  
Review
Scanning Hyperspectral Imaging for In Situ Biogeochemical Analysis of Lake Sediment Cores: Review of Recent Developments
by Paul D. Zander, Giulia Wienhues and Martin Grosjean
J. Imaging 2022, 8(3), 58; https://doi.org/10.3390/jimaging8030058 - 25 Feb 2022
Cited by 16 | Viewed by 5368
Abstract
Hyperspectral imaging (HSI) in situ core scanning has emerged as a valuable and novel tool for rapid and non-destructive biogeochemical analysis of lake sediment cores. Variations in sediment composition can be assessed directly from fresh sediment surfaces at ultra-high-resolution (40–300 μm measurement resolution) [...] Read more.
Hyperspectral imaging (HSI) in situ core scanning has emerged as a valuable and novel tool for rapid and non-destructive biogeochemical analysis of lake sediment cores. Variations in sediment composition can be assessed directly from fresh sediment surfaces at ultra-high-resolution (40–300 μm measurement resolution) based on spectral profiles of light reflected from sediments in visible, near infrared, and short-wave infrared wavelengths (400–2500 nm). Here, we review recent methodological developments in this new and growing field of research, as well as applications of this technique for paleoclimate and paleoenvironmental studies. Hyperspectral imaging of sediment cores has been demonstrated to effectively track variations in sedimentary pigments, organic matter, grain size, minerogenic components, and other sedimentary features. These biogeochemical variables record information about past climatic conditions, paleoproductivity, past hypolimnetic anoxia, aeolian input, volcanic eruptions, earthquake and flood frequencies, and other variables of environmental relevance. HSI has been applied to study seasonal and inter-annual environmental variability as recorded in individual varves (annually laminated sediments) or to study sedimentary records covering long glacial–interglacial time-scales (>10,000 years). Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Its Applications)
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