Special Issue "Hyperspectral Imaging: Methods and Applications"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (1 July 2020) | Viewed by 12766

Special Issue Editors

Prof. Dick Sterenborg
E-Mail Website1 Website2
Guest Editor
Department of Biomedical Engineering and Physics, Academic Medical Center, 1105 Amsterdam, The Netherlands
Interests: hyperspectral imaging; biomedical optics; optical biopsy; fiber optic spectroscopy
Special Issues, Collections and Topics in MDPI journals
Dr. Behdad Dasht Bozorg
E-Mail Website
Guest Editor
Department of Surgical Oncology, at the Nederlands Cancer Institute, 1066CX Amsterdam, The Netherlands
Interests: hyperspectral Imaging; image guided treatment; medical image analysis; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

HSI is a novel and versatile optical imaging technology that is fundamentally safe or harmless and can be applied on virtually any type of sample, tough or fragile, close or distant, dead or alive. It has great potential for rapid, non-destructive material investigation.

HSI is currently a hot topic in many fields of research: from medicine to forensic science and from biology and materials science to archeology. It is quite extraordinary to see researchers from very different fields and with different backgrounds use this technology for very diverse problems. Often, however, they encounter comparable problems and face similar challenges.

The Special Issue “Hyperspectral Imaging, Methods and Applications” is dedicated to bringing papers from the different fields together and stimulate cross-pollination. Therefore, we welcome papers from all fields of science. A special focus is on design and methodology; specialized optics, data acquisition protocols or processing schemes, algorithm development, as well as novel applications of HSI or application methods for HSI.

Prof. Dick Sterenborg
Dr. Behdad Dasht Bozorg
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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 2300 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
  • diffuse reflectance
  • machine learning

Published Papers (10 papers)

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Research

Article
Non-Linear Spectral Unmixing for the Estimation of the Distribution of Graphene Oxide Deposition on 3D Printed Composites
Appl. Sci. 2020, 10(21), 7792; https://doi.org/10.3390/app10217792 - 03 Nov 2020
Viewed by 585
Abstract
Hyperspectral analysis is a well-established technique that can be suitably implemented in several application fields, including materials science. This approach allows us to deal with data samples containing spatial and spectral information at very high resolution, thus enabling us to evaluate materials properties [...] Read more.
Hyperspectral analysis is a well-established technique that can be suitably implemented in several application fields, including materials science. This approach allows us to deal with data samples containing spatial and spectral information at very high resolution, thus enabling us to evaluate materials properties at a nanoscale level. As a proof of concept, hyperspectral imaging was here considered to investigate 3D printed polymer matrix composites, considering graphene oxide (GO) as a nanofiller. Commercial polycaprolactone and polylactic acid filaments were firstly treated with GO to be then printed into testing specimens. Raman analysis was performed to assess the GO distribution on samples surface by mapping different regions of interest and the collected data were the input of a custom-made algorithm for hyperspectral image analysis, tailored to detect the GO signature. Findings showed a valuable matching to Raman maps and were also characterized by the positive feature of avoiding to set specific conditions to perform the investigation as GO Raman distribution was carried out by fixing the wavenumber at 1580 cm−1, which is representative of the G band of the nanofiller. This occurrence might lead to an uneven intensity representation related to possible peak shifts which can bias the acquired results. Differently, hyperspectral imaging needs a minimal set of data input, i.e., the spectral signatures of neat materials, to directly identify the searched nanomaterial. More in-depth investigations need to be performed to fully validate the proposed approach, but the here presented results already show the potential and versatility of hyperspectral analysis in the materials science field. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
A Spectral-Aware Convolutional Neural Network for Pansharpening
Appl. Sci. 2020, 10(17), 5809; https://doi.org/10.3390/app10175809 - 22 Aug 2020
Cited by 5 | Viewed by 918
Abstract
Pansharpening aims at fusing a low-resolution multiband optical (MBO) image, such as a multispectral or a hyperspectral image, with the associated high-resolution panchromatic (PAN) image to yield a high spatial resolution MBO image. Though having achieved superior performances to traditional methods, existing convolutional [...] Read more.
Pansharpening aims at fusing a low-resolution multiband optical (MBO) image, such as a multispectral or a hyperspectral image, with the associated high-resolution panchromatic (PAN) image to yield a high spatial resolution MBO image. Though having achieved superior performances to traditional methods, existing convolutional neural network (CNN)-based pansharpening approaches are still faced with two challenges: alleviating the phenomenon of spectral distortion and improving the interpretation abilities of pansharpening CNNs. In this work, we develop a novel spectral-aware pansharpening neural network (SA-PNN). On the one hand, SA-PNN employs a network structure composed of a detail branch and an approximation branch, which is consistent with the detail injection framework; on the other hand, SA-PNN strengthens processing along the spectral dimension by using a spectral-aware strategy, which involves spatial feature transforms (SFTs) coupling the approximation branch with the detail branch as well as 3D convolution operations in the approximation branch. Our method is evaluated with experiments on real-world multispectral and hyperspectral datasets, verifying its excellent pansharpening performance. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
Appl. Sci. 2020, 10(16), 5686; https://doi.org/10.3390/app10165686 - 17 Aug 2020
Cited by 2 | Viewed by 1569
Abstract
Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In [...] Read more.
Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459× and ~429× compared to the SVM scheme, while keeping constant and even slightly improving the classification performance. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples
Appl. Sci. 2020, 10(14), 4876; https://doi.org/10.3390/app10144876 - 16 Jul 2020
Cited by 10 | Viewed by 948
Abstract
In this work, we develop a multi-mode microscopic hyperspectral imager (MMHI) for the detection of biological samples in transmission imaging, reflection imaging and fluorescence mode. A hyperspectral image cube can be obtained with 5 μm spatial resolution and 3 nm spectral resolution through [...] Read more.
In this work, we develop a multi-mode microscopic hyperspectral imager (MMHI) for the detection of biological samples in transmission imaging, reflection imaging and fluorescence mode. A hyperspectral image cube can be obtained with 5 μm spatial resolution and 3 nm spectral resolution through push-broom line scanning. To avoid possible shadows produced by the high magnification objective with a short working distance, two illumination patterns are designed to ensure the co-axiality of the illumination and detection. Three experiments for the detection of zebrafish and fingerprints and the classification of disaster-causing microalgae verify the good capability and functionality of the system. Based on the detected spectra, we can observe the impacts of β-carotene and melanin in zebrafish, hemoglobin in the fingertip, and chlorophyll in microalgae, respectively. Multi-modes can be switched freely according to the application requirement and characteristics of different samples, like transmission mode for the transparent/translucent sample, reflection mode for the opaque sample and fluorescence mode for the fluorescent sample. The MMHI system also has strong potential for the non-invasive and high-speed sensing of bio or clinical samples. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification
Appl. Sci. 2020, 10(14), 4739; https://doi.org/10.3390/app10144739 - 09 Jul 2020
Cited by 17 | Viewed by 1404
Abstract
Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried out on joint spectral–spatial information. Thirdly, a minor [...] Read more.
Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried out on joint spectral–spatial information. Thirdly, a minor but still worth mentioning factor is the stopping criteria. Therefore, this study caters to all these issues using a spatial prior Fuzziness concept coupled with Multinomial Logistic Regression via a Splitting and Augmented Lagrangian (MLR-LORSAL) classifier with dual stopping criteria. This work further compares several sample selection methods with the diverse nature of classifiers i.e., probabilistic and non-probabilistic. The sample selection methods include Breaking Ties (BT), Mutual Information (MI) and Modified Breaking Ties (MBT). The comparative classifiers include Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbour (KNN) and Ensemble Learning (EL). The experimental results on three benchmark hyperspectral datasets reveal that the proposed pipeline significantly increases the classification accuracy and generalization performance. To further validate the performance, several statistical tests are also considered such as Precision, Recall and F1-Score. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery
Appl. Sci. 2020, 10(12), 4078; https://doi.org/10.3390/app10124078 - 12 Jun 2020
Cited by 11 | Viewed by 2457
Abstract
In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of [...] Read more.
In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Spectral Reflectance Characteristics and Chlorophyll Content Estimation Model of Quercus aquifolioides Leaves at Different Altitudes in Sejila Mountain
Appl. Sci. 2020, 10(10), 3636; https://doi.org/10.3390/app10103636 - 24 May 2020
Cited by 4 | Viewed by 1193
Abstract
Quercus aquifolioides is one of the most representative broad-leaved plants in Qinghai-Tibet Plateau with important ecological status. So far, understanding how to quickly estimate the chlorophyll content of plants in plateau areas is still an urgent problem. Field Spec 3 spectrometer was used [...] Read more.
Quercus aquifolioides is one of the most representative broad-leaved plants in Qinghai-Tibet Plateau with important ecological status. So far, understanding how to quickly estimate the chlorophyll content of plants in plateau areas is still an urgent problem. Field Spec 3 spectrometer was used to measure hyperspectral reflectance data of Quercus aquifolioides leaves at different altitudes, and CCI (chlorophyll relative content) of corresponding leaves was measured by a chlorophyll meter. The correlation and univariate linear fitting analysis techniques were used to establish their relationship models. The results showed that: (1) Chlorophyll relative content of Quercus aquifolioides, under different altitude gradients, were significantly different. From 2905 m to 3500 m, chlorophyll relative content increased first and then decreased. Altitude 3300 m was the most suitable growth area. (2) In 350~550 nm, the spectral reflectance was 3500 m > 3300 m > 2905 m. In 750~1100 nm, the spectral reflectivity was 2905 m > 3500 m > 3300 m. (3) There were 4 main reflection peaks and 5 main absorption valleys in the leaf surface spectral reflection curve. While, 750~1400 nm was the sensitive range of leaf spectral response of Quercus aquifolioides. (4) The red edge position and red valley position moved to short wave direction with the increase of altitude, while the yellow edge position and green peak position moved to long wave direction first and then to short wave direction. (5) The correlation curve between the original spectrum and the CCI value was the best between the wavelengths 509~650 nm. The correlation between the first derivative spectrum and CCI value was the best and most stable at 450~500 nm. The green peak reflectance was most sensitive to the relative chlorophyll content of Quercus aquifolioides. The estimation model R2 of green peak reflectance was the highest (y = 206.98e−10.85x, R2 = 0.8523), and the prediction accuracy was 95.85%. The research results can provide some technical and theoretical support for the protection of natural Quercus aquifolioides forests in Tibet. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
Appl. Sci. 2020, 10(8), 2851; https://doi.org/10.3390/app10082851 - 20 Apr 2020
Cited by 1 | Viewed by 913
Abstract
A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear [...] Read more.
A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
Appl. Sci. 2020, 10(4), 1261; https://doi.org/10.3390/app10041261 - 13 Feb 2020
Cited by 2 | Viewed by 859
Abstract
Superpixel segmentation (SS) methods have been proven to be feasible in improving the performance of hybrid algorithms on hyperspectral images (HSIs). In this paper, a superpixel segmentation algorithm based on the information measures with color histogram driving (IM-CHD) was proposed. First, Shannon entropy [...] Read more.
Superpixel segmentation (SS) methods have been proven to be feasible in improving the performance of hybrid algorithms on hyperspectral images (HSIs). In this paper, a superpixel segmentation algorithm based on the information measures with color histogram driving (IM-CHD) was proposed. First, Shannon entropy was applied to measure the image information and preliminarily select spectral bands. Mutual information (MI) is derived from the concept of entropy and measures the statistical dependence between two random variables. Also, MI can effectively identify the redundant spectral bands. Therefore, in this paper, both MI and color matching functions (CMF) were used to select the most useful spectral bands. Second, the selected spectral bands were combined into a false color image containing the main spectral information. A local optimization algorithm named “hill climbing” was used to achieve the superpixel segmentation. Finally, parameter selection experiments and comparative experiments were performed on two hyperspectral data sets. The experimental results showed that the IM-CHD method was more efficient and accurate than other state-of-the-art methods. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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Article
Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods
Appl. Sci. 2020, 10(3), 1173; https://doi.org/10.3390/app10031173 - 10 Feb 2020
Cited by 11 | Viewed by 1450
Abstract
Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm [...] Read more.
Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
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