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Hyperspectral Imaging Sensing and Analysis
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Hyperspectral Imaging Sensing and Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 30986

Special Issue Editor

Dr. Tingkui Mu

E-Mail Website
Guest Editor
School of Physics, Xi'an Jiaotong University, Xi'an, China
Interests: integrated acquisition of multi-dimensional optical information; multidimensional optical information applications multidimensional; optical information processing; target detection and recognition

Special Issue Information

Dear Colleagues,

Hyperspectral imaging is an emerging field of electro-optical and infrared remote sensing. It employs hundreds of contiguous bands to detect and identify a variety of natural and human-made materials, towards ground-cover classification, mineral exploration, and agricultural assessment. Driven by the growing needs of hyperspectral imaging, the theory, instrument, and information interpretation are constantly developing. Advancements in sensing, analysis, and processing technology have reached a level that allows hyperspectral imaging to be more widely applied to remote sensing problems.

The main goal of this Special Issue is to provide a specialized platform for researchers dedicated to this field where they can share their important discoveries, theoretical and experimental advances, technical breakthroughs, methodological innovations, application developments, viewpoints, and perspectives to the community of Hyperspectral Imaging Sensing and Analysis.

In this Research Topic, authors are welcomed to contribute submissions of original research, reviews, mini-reviews, and perspective articles in the themes including, but not limited to:

  1. Computational hyperspectral imaging and reconstruction methods.
  2. Miniature or mobile hyperspectral imaging system.
  3. Airborne hyperspectral imaging system.
  4. Hyperspectral imaging using unmanned aerial vehicle platform.
  5. Calibration methods for kinds of hyperspectral imaging system.
  6. Application processing of hyperspectral imaging.

Dr. Tingkui Mu
Guest Editor

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • hyperspectral imaging
  • imaging spectrometer
  • hyperspectral image

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Published Papers (6 papers)

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Research

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21 pages, 15261 KiB  
Article
A Vicarious Technique for Understanding and Diagnosing Hyperspectral Spatial Misregistration
by David N. Conran and Emmett J. Ientilucci
Sensors 2023, 23(9), 4333; https://doi.org/10.3390/s23094333 - 27 Apr 2023
Cited by 1 | Viewed by 1870
Abstract
Pushbroom hyperspectral imaging (HSI) systems intrinsically measure our surroundings by leveraging 1D spatial imaging, where each pixel contains a unique spectrum of the observed materials. Spatial misregistration is an important property of HSI systems because it defines the spectral integrity of spatial pixels [...] Read more.
Pushbroom hyperspectral imaging (HSI) systems intrinsically measure our surroundings by leveraging 1D spatial imaging, where each pixel contains a unique spectrum of the observed materials. Spatial misregistration is an important property of HSI systems because it defines the spectral integrity of spatial pixels and requires characterization. The IEEE P4001 Standards Association committee has defined laboratory-based methods to test the ultimate limit of HSI systems but negates any impacts from mounting and flying the instruments on airborne platforms such as unmanned aerial vehicles (UAV’s) or drones. Our study was designed to demonstrate a novel vicarious technique using convex mirrors to bridge the gap between laboratory and field-based HSI performance testing with a focus on extracting hyperspectral spatial misregistration. A fast and simple extraction technique is proposed for estimating the sampled Point Spread Function’s width, along with keystone, as a function of wavelength for understanding the key contributors to hyperspectral spatial misregistration. With the ease of deploying convex mirrors, off-axis spatial misregistration is assessed and compared with on-axis behavior, where the best performance is often observed. In addition, convex mirrors provide an easy methodology to exploit ortho-rectification errors related to fixed pushbroom HSI systems, which we will show. The techniques discussed in this study are not limited to drone-based systems but can be easily applied to other airborne or satellite-based systems. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Sensing and Analysis)
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19 pages, 14827 KiB  
Article
Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories
by Kiah Edwards, Louwrens C. Hoffman, Marena Manley and Paul J. Williams
Sensors 2023, 23(2), 697; https://doi.org/10.3390/s23020697 - 7 Jan 2023
Cited by 5 | Viewed by 2453
Abstract
South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and [...] Read more.
South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and expensive methods. Eight hundred beef patties (ca. 100 g) were made and analysed to assess the potential of NIR-HSI to distinguish between the four patty categories (200 patties per category): premium ‘ground patty’; regular ‘burger patty’; ‘value-burger/patty’ and the ‘econo-burger’/’budget’. Hyperspectral images were acquired with a HySpex SWIR-384 (short-wave infrared) imaging system using the Breeze® acquisition software, in the wavelength range of 952–2517 nm, after which the data was analysed using image analysis, multivariate techniques and machine learning algorithms. It was possible to distinguish between the four patty categories with accuracies ≥97%, indicating that NIR-HSI offers an accurate and reliable solution for the rapid identification and authentication of processed beef patties. Furthermore, this study has the potential of providing an alternative to the current authentication methods, thus contributing to the authenticity and fair-trade of processed meat products locally and internationally. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Sensing and Analysis)
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20 pages, 12341 KiB  
Article
The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice
by Rosa Maria Cavalli
Sensors 2023, 23(1), 454; https://doi.org/10.3390/s23010454 - 1 Jan 2023
Cited by 7 | Viewed by 2211
Abstract
Following the success of the first hyperspectral sensor, the evaluation of hyperspectral image capability became a challenge in research, which mainly focused on improving image pre-processing and processing steps to minimize their errors, whereas in this study, the focus was on the weight [...] Read more.
Following the success of the first hyperspectral sensor, the evaluation of hyperspectral image capability became a challenge in research, which mainly focused on improving image pre-processing and processing steps to minimize their errors, whereas in this study, the focus was on the weight of hyperspectral sensor characteristics on image capability in order to distinguish this effect from errors caused by image pre-processing and processing steps and improve our knowledge of errors. For these purposes, two satellite hyperspectral sensors with similar spatial and spectral characteristics (Hyperion and PRISMA) were compared with corresponding synthetic images, and the city of Venice was selected as the study area. After creating the synthetic images, the errors in the simulation of Hyperion and PRISMA images were evaluated (1.6 and 1.1%, respectively). The same spectral unmixing procedure was performed using real and synthetic images, and their accuracies were compared. The spectral accuracies in root mean square error were equal to 0.017 and 0.016, respectively. In addition, 72.3 and 77.4% of these values were related to sensor characteristics. The spatial accuracies in the mean absolute error were equal to 3.93 and 3.68, respectively. A total of 55.6 and 59.0% of these values were related to sensor characteristics, and 22.6 and 22.3% were related to co-localization and spatial resampling errors. The difference between the radiometric precision values of the sensors was 6.81 and 5.91% regarding the spectral and spatial accuracies of Hyperion image. In conclusion, the results of this study showed that the combined use of two or more real hyperspectral images with similar characteristics and their synthetic images quantifies the weight of hyperspectral sensor characteristics on their image capability and improves our knowledge regarding processing errors, and thus image capability. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Sensing and Analysis)
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13 pages, 4709 KiB  
Communication
High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios
by Mary B. Stuart, Matthew Davies, Matthew J. Hobbs, Tom D. Pering, Andrew J. S. McGonigle and Jon R. Willmott
Sensors 2022, 22(12), 4652; https://doi.org/10.3390/s22124652 - 20 Jun 2022
Cited by 33 | Viewed by 7823
Abstract
High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider [...] Read more.
High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider implications, limiting data collection opportunities, and therefore our knowledge, across a wide range of environments. In this article we introduce a low-cost alternative to the currently available instrumentation. This instrument provides hyperspectral datasets capable of resolving spectral variations in mm-scale targets, that cannot typically be resolved with many existing low-cost hyperspectral imaging alternatives. Instrument metrology is provided, and its efficacy is demonstrated within a mineralogy-based environmental monitoring application highlighting it as a valuable addition to the field of low-cost hyperspectral imaging. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Sensing and Analysis)
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Review

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26 pages, 5759 KiB  
Review
Deep Learning in Medical Hyperspectral Images: A Review
by Rong Cui, He Yu, Tingfa Xu, Xiaoxue Xing, Xiaorui Cao, Kang Yan and Jiexi Chen
Sensors 2022, 22(24), 9790; https://doi.org/10.3390/s22249790 - 13 Dec 2022
Cited by 35 | Viewed by 10137
Abstract
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper [...] Read more.
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Sensing and Analysis)
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18 pages, 597 KiB  
Review
Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging
by Shuan-Yu Huang, Arvind Mukundan, Yu-Ming Tsao, Youngjo Kim, Fen-Chi Lin and Hsiang-Chen Wang
Sensors 2022, 22(19), 7308; https://doi.org/10.3390/s22197308 - 26 Sep 2022
Cited by 34 | Viewed by 5081
Abstract
Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical [...] Read more.
Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Sensing and Analysis)
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