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Hyperspectral Imaging and Sensing

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 12320

Special Issue Editors

College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: multispectral imaging; image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Ningbo Research Institute, Zhejiang University, Ningbo 315100, China
Interests: multispectral imaging; image processing
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Survey of Finland, 02430 Masala, Finland
Interests: radiometric calibration for hyperspectral imaging; calibration site design

Special Issue Information

Dear Colleagues,

Hyperspectral imaging (HSI) is a new analytical technique based on spectroscopy that analyzes a wide spectrum of light. It collects hundreds of images at different wavelengths for the same spatial area. In contrast, the human eye has only three color receptors, for blue, green, and red. Hyperspectral imaging measures the continuous spectrum of light for each pixel of the scene with fine wavelength resolution, not only in the visible but also in the near-infrared. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene with the purpose of finding objects, identifying materials, or detecting processes.

Each material possesses a specific spectral signature that can be employed as a “fingerprint” for its unique identification. Therefore, hyperspectral imaging finds a wide range of applications in electro-optical and remote sensing. We welcome the submission of contributions addressing state-of-the-art developments and methodologies, as well as applications of Hyperspectral Imaging and Sensing in the future.

Manuscripts should contain both theoretical and practical/experimental results. Potential topics include but are not limited to the following: hyperspectral imaging, hyperspectral sensors, imaging spectroscopy, remote sensing, radiometric calibration, calibration site design, hyperspectral image processing, and hyperspectral machine vision.

Prof. Dr. Hui-liang Shen
Dr. Siyuan Cao
Dr. Yuwei Chen
Dr. Eero Ahokas
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. 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.

Published Papers (8 papers)

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Research

16 pages, 2239 KiB  
Article
An Adaptive Partial Least-Squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging
by Adeyemi O. Adegbenjo, Li Liu and Michael O. Ngadi
Sensors 2024, 24(5), 1485; https://doi.org/10.3390/s24051485 - 24 Feb 2024
Viewed by 458
Abstract
Partial least-squares (PLS) regression is a well known chemometric method used for predictive modelling, especially in the presence of many variables. Although PLS was not initially developed as a technique for classification tasks, scientists have reportedly used this approach successfully for discrimination purposes. [...] Read more.
Partial least-squares (PLS) regression is a well known chemometric method used for predictive modelling, especially in the presence of many variables. Although PLS was not initially developed as a technique for classification tasks, scientists have reportedly used this approach successfully for discrimination purposes. Whereas some non-supervised learning approaches, including, but not limited to, PCA and k-means clustering, do well in identifying/understanding grouping and clustering patterns in multidimensional data, they are limited when the end target is discrimination, making PLS a preferable alternative. Hyperspectral imaging data on a total of 672 fertilized chicken eggs, consisting of 336 white eggs and 336 brown eggs, were used in this study. Hyperspectral images in the NIR region of the 900–1700 nm wavelength range were captured prior to incubation on day 0 and on days 1–4 after incubation. Eggs were candled on incubation day 5 and broken out on day 10 to confirm fertility. While a total number of 312 and 314 eggs were found to be fertile in the brown and white egg batches, respectively, the total number of non-fertile eggs in the same set of batches was 23 and 21, respectively. Spectral information was extracted from a segmented region of interest (ROI) of each hyperspectral image and spectral transmission characteristics were obtained by averaging the spectral information. A moving-thresholding technique was implemented for discrimination based on PLS regression results on the calibration set. With true positive rates (TPRs) of up to 100% obtained at selected threshold values of between 0.50 and 0.85 and on different days of incubation, the results indicate that the proposed PLS technique can accurately discriminate between fertile and non-fertile eggs. The adaptive PLS approach was, thereby, presented as suitable for handling hyperspectral imaging-based chicken egg fertility data. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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16 pages, 6784 KiB  
Article
A Customisable Data Acquisition System for Open-Source Hyperspectral Imaging
by Yiwei Mao, Christopher H. Betters, Samuel Garske, Jeremy Randle, K. C. Wong, Iver H. Cairns and Bradley J. Evans
Sensors 2023, 23(20), 8622; https://doi.org/10.3390/s23208622 - 21 Oct 2023
Viewed by 1334
Abstract
Hyperspectral imagers, or imaging spectrometers, are used in many remote sensing environmental studies in fields such as agriculture, forestry, geology, and hydrology. In recent years, compact hyperspectral imagers were developed using commercial-off-the-shelf components, but there are not yet any off-the-shelf data acquisition systems [...] Read more.
Hyperspectral imagers, or imaging spectrometers, are used in many remote sensing environmental studies in fields such as agriculture, forestry, geology, and hydrology. In recent years, compact hyperspectral imagers were developed using commercial-off-the-shelf components, but there are not yet any off-the-shelf data acquisition systems on the market to deploy them. The lack of a self-contained data acquisition system with navigation sensors is a challenge that needs to be overcome to successfully deploy these sensors on remote platforms such as drones and aircraft. Our work is the first successful attempt to deploy an entirely open-source system that is able to collect hyperspectral and navigation data concurrently for direct georeferencing. In this paper, we describe a low-cost, lightweight, and deployable data acquisition device for the open-source hyperspectral imager (OpenHSI). We utilised commercial-off-the-shelf hardware and open-source software to create a compact data acquisition device that can be easily transported and deployed. The device includes a microcontroller and a custom-designed PCB board to interface with ancillary sensors and a Raspberry Pi 4B/NVIDIA Jetson. We demonstrated our data acquisition system on a Matrice M600 drone at a beach in Sydney, Australia, collecting timestamped hyperspectral, navigation, and orientation data in parallel. Using the navigation and orientation data, the hyperspectral data were georeferenced. While the entire system including the pushbroom hyperspectral imager and housing weighed 735 g, it was designed to be easy to assemble and modify. This low-cost, customisable, deployable data acquisition system provides a cost-effective solution for the remote sensing of hyperspectral data for everyone. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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13 pages, 2059 KiB  
Article
Monitoring Distribution of the Therapeutic Agent Dimethyl Sulfoxide via Solvatochromic Shift of Albumin-Bound Indocyanine Green
by Jaedu Cho, Farouk Nouizi, Chang-Seok Kim and Gultekin Gulsen
Sensors 2023, 23(18), 7728; https://doi.org/10.3390/s23187728 - 07 Sep 2023
Viewed by 846
Abstract
We recently developed a novel hyperspectral excitation-resolved near-infrared fluorescence imaging system (HER-NIRF) based on a continuous-wave wavelength-swept laser. In this study, this technique is applied to measure the distribution of the therapeutic agent dimethyl sulfoxide (DMSO) by utilizing solvatochromic shift in the spectral [...] Read more.
We recently developed a novel hyperspectral excitation-resolved near-infrared fluorescence imaging system (HER-NIRF) based on a continuous-wave wavelength-swept laser. In this study, this technique is applied to measure the distribution of the therapeutic agent dimethyl sulfoxide (DMSO) by utilizing solvatochromic shift in the spectral profile of albumin-bound Indocyanine green (ICG). Using wide-field imaging in turbid media, complex dynamics of albumin-bound ICG are measured in mixtures of dimethyl sulfoxide (DMSO) and water. Phantom experiments are conducted to evaluate the performance of the HER-NIRF system. The results show that the distribution of DMSO can be visualized in the wide-field reflection geometry. One of the main purposes of the DMSO is to act as a carrier for other drugs, enhancing their effects by facilitating skin penetration. Understanding the solubility and permeability of drugs in vivo is very important in drug discovery and development. Hence, this HER-NIRF technique has great potential to advance the utilization of the therapeutic agent DMSO by mapping its distribution via the solvatochromic shift of ICG. By customizing the operational wavelength range, this system can be applied to any other fluorophores in the near-infrared region and utilized for a wide variety of drug delivery studies. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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20 pages, 58299 KiB  
Article
A Rehabilitation of Pixel-Based Spectral Reconstruction from RGB Images
by Yi-Tun Lin and Graham D. Finlayson
Sensors 2023, 23(8), 4155; https://doi.org/10.3390/s23084155 - 21 Apr 2023
Cited by 3 | Viewed by 1749
Abstract
Recently, many deep neural networks (DNN) have been proposed to solve the spectral reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek to learn the relationship between an RGB viewed in a given spatial context and its corresponding spectra. Significantly, it [...] Read more.
Recently, many deep neural networks (DNN) have been proposed to solve the spectral reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek to learn the relationship between an RGB viewed in a given spatial context and its corresponding spectra. Significantly, it is argued that the same RGB can map to different spectra depending on the context with respect to which it is seen and, more generally, that accounting for spatial context leads to improved SR. However, as it stands, DNN performance is only slightly better than the much simpler pixel-based methods where spatial context is not used. In this paper, we present a new pixel-based algorithm called A++ (an extension of the A+ sparse coding algorithm). In A+, RGBs are clustered, and within each cluster, a designated linear SR map is trained to recover spectra. In A++, we cluster the spectra instead in an attempt to ensure neighboring spectra (i.e., spectra in the same cluster) are recovered by the same SR map. A polynomial regression framework is developed to estimate the spectral neighborhoods given only the RGB values in testing, which in turn determines which mapping should be used to map each testing RGB to its reconstructed spectrum. Compared to the leading DNNs, not only does A++ deliver the best results, it is parameterized by orders of magnitude fewer parameters and has a significantly faster implementation. Moreover, in contradistinction to some DNN methods, A++ uses pixel-based processing, which is robust to image manipulations that alter the spatial context (e.g., blurring and rotations). Our demonstration on the scene relighting application also shows that, while SR methods, in general, provide more accurate relighting results compared to the traditional diagonal matrix correction, A++ provides superior color accuracy and robustness compared to the top DNN methods. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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13 pages, 3539 KiB  
Article
Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images
by Xing Qin, Chenxiao Lai, Zejun Pan, Mingzhong Pan, Yun Xiang and Yikun Wang
Sensors 2023, 23(7), 3645; https://doi.org/10.3390/s23073645 - 31 Mar 2023
Viewed by 1169
Abstract
The egg production of laying hens is crucial to breeding enterprises in the laying hen breeding industry. However, there is currently no systematic or accurate method to identify low-egg-production-laying hens in commercial farms, and the majority of these hens are identified by breeders [...] Read more.
The egg production of laying hens is crucial to breeding enterprises in the laying hen breeding industry. However, there is currently no systematic or accurate method to identify low-egg-production-laying hens in commercial farms, and the majority of these hens are identified by breeders based on their experience. In order to address this issue, we propose a method that is widely applicable and highly precise. First, breeders themselves separate low-egg-production-laying hens and normal-laying hens. Then, under a halogen lamp, hyperspectral images of the two different types of hens are captured via hyperspectral imaging equipment. The vertex component analysis (VCA) algorithm is used to extract the cockscomb end member spectrum to obtain the cockscomb spectral feature curves of low-egg-production-laying hens and normal ones. Next, fast continuous wavelet transform (FCWT) is employed to analyze the data of the feature curves in order to obtain the two-dimensional spectral feature image dataset. Finally, referring to the two-dimensional spectral image dataset of the low-egg-production-laying hens and normal ones, we developed a deep learning model based on a convolutional neural network (CNN). When we tested the model’s accuracy by using the prepared dataset, we found that it was 0.975 percent accurate. This outcome demonstrates our identification method, which combines hyperspectral imaging technology, an FCWT data analysis method, and a CNN deep learning model, and is highly effective and precise in laying-hen breeding plants. Furthermore, the attempt to use FCWT for the analysis and processing of hyperspectral data will have a significant impact on the research and application of hyperspectral technology in other fields due to its high efficiency and resolution characteristics for data signal analysis and processing. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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14 pages, 4372 KiB  
Communication
Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm
by Arvind Mukundan, Yu-Ming Tsao, Wen-Min Cheng, Fen-Chi Lin and Hsiang-Chen Wang
Sensors 2023, 23(4), 2026; https://doi.org/10.3390/s23042026 - 10 Feb 2023
Cited by 11 | Viewed by 3103
Abstract
In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB images to HSI images is designed using the Raspberry Pi environment. A Windows-based Python application is also developed to control the Raspberry Pi camera and processor. The mean gray values (MGVs) [...] Read more.
In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB images to HSI images is designed using the Raspberry Pi environment. A Windows-based Python application is also developed to control the Raspberry Pi camera and processor. The mean gray values (MGVs) of two distinct regions of interest (ROIs) are selected from three samples of 100 NTD Taiwanese currency notes and compared with three samples of counterfeit 100 NTD notes. Results suggest that the currency notes can be easily differentiated on the basis of MGV values within shorter wavelengths, between 400 nm and 500 nm. However, the MGV values are similar in longer wavelengths. Moreover, if an ROI has a security feature, then the classification method is considerably more efficient. The key features of the module include portability, lower cost, a lack of moving parts, and no processing of images required. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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13 pages, 3141 KiB  
Article
Prediction of Honeydew Contaminations on Cotton Samples by In-Line UV Hyperspectral Imaging
by Mohammad Al Ktash, Mona Stefanakis, Frank Wackenhut, Volker Jehle, Edwin Ostertag, Karsten Rebner and Marc Brecht
Sensors 2023, 23(1), 319; https://doi.org/10.3390/s23010319 - 28 Dec 2022
Cited by 1 | Viewed by 1500
Abstract
UV hyperspectral imaging (225 nm–410 nm) was used to identify and quantify the honeydew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, [...] Read more.
UV hyperspectral imaging (225 nm–410 nm) was used to identify and quantify the honeydew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R2cv = 0.80 and low RMSECV = 0.01 g for the validation. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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15 pages, 5292 KiB  
Article
Spectral Reflectance Reconstruction of Organ Tissue Based on Metameric Black and Lattice Regression
by Yang Chen, Siyuan Zhang and Lihao Xu
Sensors 2022, 22(23), 9405; https://doi.org/10.3390/s22239405 - 02 Dec 2022
Viewed by 1102
Abstract
In this study, a new approach is proposed for the restoration of reflectance information on organ samples using a commercial camera. This novel approach is comprised of three stages. In the first stage, a color clustering method is utilized to extract the representative [...] Read more.
In this study, a new approach is proposed for the restoration of reflectance information on organ samples using a commercial camera. This novel approach is comprised of three stages. In the first stage, a color clustering method is utilized to extract the representative colors of the organ samples as well as their corresponding spectral reflectance. In the second stage, the spectral reflectance is decomposed into two separate parts, i.e., the fundamental stimulus spectrum and the metameric black following the matrix-R theory, and the latter is further utilized to form a look-up table (LUT) via a lattice regression model. Finally, the reflectance information can be easily retrieved by referring to the newly built LUT. The performance of the proposed method was investigated, along with that of six other commonly adopted methods, through a physical experiment using real, measured organ samples. The results demonstrate that the proposed method outperformed all the other methods in terms of both colorimetric and spectral metrics, indicating that it is a promising strategy for organ sample reflectance restoration. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Sensing)
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