Special Issue "The Future of Hyperspectral Imaging"

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (26 October 2018).

Special Issue Editor

Guest Editor
Dr. Stefano Selci

IFN-CNR, 00133 Roma, Italy
Website | E-Mail
Interests: spectroscopy; scanning microscopy; hyperspectral imaging

Special Issue Information

Dear Colleagues,

Hyperspectral-based techniques (HSI) are pervading many, and increasing, fields of application. HSI began as a quite obvious tool within remote airborne observations; for instance, to determine land resources. The rapid development of spectroscopic hardware allowed fundamental passage of HSI from multispectral analyses (a few spectral lines), up to the full control and capture of spectral continuous ranges, while expanding its realm to food, biology, medicine, forensics, and art observation, just to name a few. The remarkable mix of the information (often represented as “hypercubes”) is at the same time spectroscopic (wavelength axis), structural (three axes), and plus time (a further axis). The structural part represents a range of information that can be within at least six orders of magnitude between micrometers (using HSI methods within microscopes, also of confocal type) and meters, while the usually-available large spectral range can be further functionally increased by adding fluorescence and Raman spectroscopies.

However, the rapid increase in the application areas will require a much higher speed in acquisition, clever data elaboration (e.g., neural networking methods are already used to safely assign local spectroscopic fingerprints to HIS images ), new hardware, and new ideas. There is a need to have effective tools, for instance, to make food analyses on real stocks, in real time and compatible with the daily products’ market, or make diagnoses on cells to reveal cancer during a routine medical check, without the need for a long wait.

Which advancements will be eventually more productive and innovative in this field?

We request contributions presenting techniques (methods, tools, ideas, or even market evaluations) that will contribute to the future roadmap of HSI, as well as concepts for significantly innovative objectives in HSI techniques.  Scientifically founded innovative and speculative research lines are welcome for proposal and evaluation.

Dr. Stefano Selci
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 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
  • Real time imaging and spectroscopies
  • Medical imaging by HSI
  • HSI for biology
  • Remote sensing
  • Hyperspectral microscopy
  • Fluorescence hyperspectral imaging
  • Raman hyperspectral imaging
  • Infrared hyperspectral imaging
  • Nanoscale imaging in HSI
  • Statistical methods for HSI
  • Hyperspectral data mining and compression
  • Hardware solutions for compact HSI instrumentation
  • Hyperspectral market forecast

Published Papers (12 papers)

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Research

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Open AccessArticle
Prediction of the Leaf Primordia of Potato Tubers Using Sensor Fusion and Wavelength Selection
J. Imaging 2019, 5(1), 10; https://doi.org/10.3390/jimaging5010010
Received: 10 November 2018 / Revised: 29 December 2018 / Accepted: 3 January 2019 / Published: 9 January 2019
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Abstract
The sprouting of potato tubers during storage is a significant problem that suppresses obtaining high quality seeds or fried products. In this study, the potential of fusing data obtained from visible (VIS)/near-infrared (NIR) spectroscopic and hyperspectral imaging systems was investigated, to improve the [...] Read more.
The sprouting of potato tubers during storage is a significant problem that suppresses obtaining high quality seeds or fried products. In this study, the potential of fusing data obtained from visible (VIS)/near-infrared (NIR) spectroscopic and hyperspectral imaging systems was investigated, to improve the prediction of primordial leaf count as a significant sign for tubers sprouting. Electronic and lab measurements were conducted on whole tubers of Frito Lay 1879 (FL1879) and Russet Norkotah (R.Norkotah) potato cultivars. The interval partial least squares (IPLS) technique was adopted to extract the most effective wavelengths for both systems. Linear regression was utilized using partial least squares regression (PLSR), and the best calibration model was chosen using four-fold cross-validation. Then the prediction models were obtained using separate test data sets. Prediction results were enhanced compared with those obtained from individual systems’ models. The values of the correlation coefficient (the ratio between performance to deviation, or r(RPD)) were 0.95(3.01) and 0.9s6(3.55) for FL1879 and R.Norkotah, respectively, which represented a feasible improvement by 6.7%(35.6%) and 24.7%(136.7%) for FL1879 and R.Norkotah, respectively. The proposed study shows the possibility of building a rapid, noninvasive, and accurate system or device that requires minimal or no sample preparation to track the sprouting activity of stored potato tubers. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
Hyperspectral Imaging as Powerful Technique for Investigating the Stability of Painting Samples
J. Imaging 2019, 5(1), 8; https://doi.org/10.3390/jimaging5010008
Received: 26 October 2018 / Revised: 21 November 2018 / Accepted: 26 December 2018 / Published: 3 January 2019
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Abstract
The aim of this work is to present the utilization of Hyperspectral Imaging for studying the stability of painting samples to simulated solar radiation, in order to evaluate their use in the restoration field. In particular, ready-to-use commercial watercolours and powder pigments were [...] Read more.
The aim of this work is to present the utilization of Hyperspectral Imaging for studying the stability of painting samples to simulated solar radiation, in order to evaluate their use in the restoration field. In particular, ready-to-use commercial watercolours and powder pigments were tested, with these last ones being prepared for the experimental by gum Arabic in order to propose a possible substitute for traditional reintegration materials. Samples were investigated through Hyperspectral Imaging in the short wave infrared range before and after artificial ageing procedure performed in Solar Box chamber under controlled conditions. Data were treated and elaborated in order to evaluate the sensitivity of the Hyperspectral Imaging technique to identify the variations on paint layers, induced by photo-degradation, before they could be detected by eye. Furthermore, a supervised classification method for monitoring the painted surface changes, adopting a multivariate approach was successfully applied. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
A Low-Rate Video Approach to Hyperspectral Imaging of Dynamic Scenes
J. Imaging 2019, 5(1), 6; https://doi.org/10.3390/jimaging5010006
Received: 10 November 2018 / Revised: 14 December 2018 / Accepted: 26 December 2018 / Published: 31 December 2018
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Abstract
The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200–400 Hz range). These data acquisition rates present an opportunity to acquire [...] Read more.
The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200–400 Hz range). These data acquisition rates present an opportunity to acquire full hyperspectral scenes at rapid rates, enabling the use of traditional push-broom imaging systems as low-rate video hyperspectral imaging systems. This paper provides an overview of the design of an integrated system that produces low-rate video hyperspectral image sequences by merging a hyperspectral line scanner, operating in the visible and near infra-red, with a high-speed pan-tilt system and an integrated IMU-GPS that provides system pointing. The integrated unit is operated from atop a telescopic mast, which also allows imaging of the same surface area or objects from multiple view zenith directions, useful for bi-directional reflectance data acquisition and analysis. The telescopic mast platform also enables stereo hyperspectral image acquisition, and therefore, the ability to construct a digital elevation model of the surface. Imaging near the shoreline in a coastal setting, we provide an example of hyperspectral imagery time series acquired during a field experiment in July 2017 with our integrated system, which produced hyperspectral image sequences with 371 spectral bands, spatial dimensions of 1600 × 212, and 16 bits per pixel, every 0.67 s. A second example times series acquired during a rooftop experiment conducted on the Rochester Institute of Technology campus in August 2017 illustrates a second application, moving vehicle imaging, with 371 spectral bands, 16 bit dynamic range, and 1600 × 300 spatial dimensions every second. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers
J. Imaging 2018, 4(12), 144; https://doi.org/10.3390/jimaging4120144
Received: 22 September 2018 / Revised: 1 December 2018 / Accepted: 4 December 2018 / Published: 7 December 2018
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Abstract
Diabetic foot ulcers are a major complication of diabetes and present a considerable burden for both patients and health care providers. As healing often takes many months, a method of determining which ulcers would be most likely to heal would be of great [...] Read more.
Diabetic foot ulcers are a major complication of diabetes and present a considerable burden for both patients and health care providers. As healing often takes many months, a method of determining which ulcers would be most likely to heal would be of great value in identifying patients who require further intervention at an early stage. Hyperspectral imaging (HSI) is a tool that has the potential to meet this clinical need. Due to the different absorption spectra of oxy- and deoxyhemoglobin, in biomedical HSI the majority of research has utilized reflectance spectra to estimate oxygen saturation (SpO2) values from peripheral tissue. In an earlier study, HSI of 43 patients with diabetic foot ulcers at the time of presentation revealed that ulcer healing by 12 weeks could be predicted by the assessment of SpO2 calculated from these images. Principal component analysis (PCA) is an alternative approach to analyzing HSI data. Although frequently applied in other fields, mapping of SpO2 is more common in biomedical HSI. It is therefore valuable to compare the performance of PCA with SpO2 measurement in the prediction of wound healing. Data from the same study group have now been used to examine the relationship between ulcer healing by 12 weeks when the results of the original HSI are analyzed using PCA. At the optimum thresholds, the sensitivity of prediction of healing by 12 weeks using PCA (87.5%) was greater than that of SpO2 (50.0%), with both approaches showing equal specificity (88.2%). The positive predictive value of PCA and oxygen saturation analysis was 0.91 and 0.86, respectively, and a comparison by receiver operating characteristic curve analysis revealed an area under the curve of 0.88 for PCA compared with 0.66 using SpO2 analysis. It is concluded that HSI may be a better predictor of healing when analyzed by PCA than by SpO2. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms
J. Imaging 2018, 4(12), 143; https://doi.org/10.3390/jimaging4120143
Received: 5 November 2018 / Revised: 26 November 2018 / Accepted: 2 December 2018 / Published: 4 December 2018
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Abstract
The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore [...] Read more.
The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
Efficient Lossless Compression of Multitemporal Hyperspectral Image Data
J. Imaging 2018, 4(12), 142; https://doi.org/10.3390/jimaging4120142
Received: 14 November 2018 / Accepted: 27 November 2018 / Published: 2 December 2018
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Abstract
Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D [...] Read more.
Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
Age Determination of Blood-Stained Fingerprints Using Visible Wavelength Reflectance Hyperspectral Imaging
J. Imaging 2018, 4(12), 141; https://doi.org/10.3390/jimaging4120141
Received: 21 October 2018 / Revised: 25 November 2018 / Accepted: 27 November 2018 / Published: 29 November 2018
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Abstract
The ability to establish the exact time a crime was committed is one of the fundamental aims of forensic science. The analysis of recovered evidence can provide information to assist in age determination, such as blood, which is one of the most commonly [...] Read more.
The ability to establish the exact time a crime was committed is one of the fundamental aims of forensic science. The analysis of recovered evidence can provide information to assist in age determination, such as blood, which is one of the most commonly encountered types of biological evidence and the most common fingerprint contaminant. There are currently no accepted methods to establish the age of a blood-stained fingerprint, so progress in this area would be of considerable benefit for forensic investigations. A novel application of visible wavelength reflectance, hyperspectral imaging (HSI), is used for the detection and age determination of blood-stained fingerprints on white ceramic tiles. Both identification and age determination are based on the unique visible absorption spectrum of haemoglobin between 400 and 680 nm and the presence of the Soret peak at 415 nm. In this study, blood-stained fingerprints were aged over 30 days and analysed using HSI. False colour aging scales were produced from a 30-day scale and a 24 h scale, allowing for a clear visual method for age estimations for deposited blood-stained fingerprints. Nine blood-stained fingerprints of varying ages deposited on one white ceramic tile were easily distinguishable using the 30-day false colour scale. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
Fusing Multiple Multiband Images
J. Imaging 2018, 4(10), 118; https://doi.org/10.3390/jimaging4100118
Received: 21 August 2018 / Revised: 5 October 2018 / Accepted: 8 October 2018 / Published: 12 October 2018
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Abstract
High-resolution hyperspectral images are in great demand but hard to acquire due to several existing fundamental and technical limitations. A practical way around this is to fuse multiple multiband images of the same scene with complementary spatial and spectral resolutions. We propose an [...] Read more.
High-resolution hyperspectral images are in great demand but hard to acquire due to several existing fundamental and technical limitations. A practical way around this is to fuse multiple multiband images of the same scene with complementary spatial and spectral resolutions. We propose an algorithm for fusing an arbitrary number of coregistered multiband, i.e., panchromatic, multispectral, or hyperspectral, images through estimating the endmember and their abundances in the fused image. To this end, we use the forward observation and linear mixture models and formulate an appropriate maximum-likelihood estimation problem. Then, we regularize the problem via a vector total-variation penalty and the non-negativity/sum-to-one constraints on the endmember abundances and solve it using the alternating direction method of multipliers. The regularization facilitates exploiting the prior knowledge that natural images are mostly composed of piecewise smooth regions with limited abrupt changes, i.e., edges, as well as coping with potential ill-posedness of the fusion problem. Experiments with multiband images constructed from real-world hyperspectral images reveal the superior performance of the proposed algorithm in comparison with the state-of-the-art algorithms, which need to be used in tandem to fuse more than two multiband images. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications
J. Imaging 2018, 4(10), 110; https://doi.org/10.3390/jimaging4100110
Received: 24 August 2018 / Revised: 14 September 2018 / Accepted: 19 September 2018 / Published: 21 September 2018
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Abstract
A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 [...] Read more.
A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 nm Nd:YAG laser and a standard pushbroom HSI camera. Depending on the lens selected, it is possible to cover large areas (e.g., field of view (FOV) = 386 mm) or to achieve high spatial resolutions (e.g., 0.02 mm). The developed Laser-HSI was used for four exemplary experiments: (a) the measurement and classification of a mixture of sulphur and naphthalene; (b) the measurement of carotenoid distribution in a carrot slice; (c) the classification of black polymer particles; and, (d) the localization of impurities on a lead zirconate titanate (PZT) piezoelectric actuator. It could be shown that the measurement data obtained were in good agreement with reference measurements taken with a high-resolution Raman microscope. Furthermore, the suitability of the measurements for classification using machine learning algorithms was also demonstrated. The developed Laser-HSI could be used in the future for complex quality control or sorting tasks where conventional HSI systems fail. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Review

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Open AccessReview
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
J. Imaging 2019, 5(5), 52; https://doi.org/10.3390/jimaging5050052
Received: 9 April 2019 / Revised: 29 April 2019 / Accepted: 2 May 2019 / Published: 8 May 2019
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Abstract
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging [...] Read more.
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessReview
Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal
J. Imaging 2019, 5(1), 3; https://doi.org/10.3390/jimaging5010003
Received: 28 October 2018 / Revised: 25 November 2018 / Accepted: 18 December 2018 / Published: 22 December 2018
Cited by 3 | PDF Full-text (11520 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging. This article provides a review of the Compressive Sensing [...] Read more.
Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging. This article provides a review of the Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) camera, its evolution, and its different applications. The CS-MUSI camera was designed within the CS framework and uses a liquid crystal (LC) phase retarder in order to modulate the spectral domain. The outstanding advantage of the CS-MUSI camera is that the entire HS image is captured from an order of magnitude fewer measurements of the sensor array, compared to conventional HS imaging methods. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessReview
Recent Trends in Compressive Raman Spectroscopy Using DMD-Based Binary Detection
J. Imaging 2019, 5(1), 1; https://doi.org/10.3390/jimaging5010001
Received: 21 November 2018 / Revised: 11 December 2018 / Accepted: 13 December 2018 / Published: 21 December 2018
Cited by 3 | PDF Full-text (3155 KB) | HTML Full-text | XML Full-text
Abstract
The collection of high-dimensional hyperspectral data is often the slowest step in the process of hyperspectral Raman imaging. With the conventional array-based Raman spectroscopy acquiring of chemical images could take hours to even days. To increase the Raman collection speeds, a number of [...] Read more.
The collection of high-dimensional hyperspectral data is often the slowest step in the process of hyperspectral Raman imaging. With the conventional array-based Raman spectroscopy acquiring of chemical images could take hours to even days. To increase the Raman collection speeds, a number of compressive detection (CD) strategies, which simultaneously sense and compress the spectral signal, have recently been demonstrated. As opposed to conventional hyperspectral imaging, where full spectra are measured prior to post-processing and imaging CD increases the speed of data collection by making measurements in a low-dimensional space containing only the information of interest, thus enabling real-time imaging. The use of single channel detectors gives the key advantage to CD strategy using optical filter functions to obtain component intensities. In other words, the filter functions are simply the optimized patterns of wavelength combinations characteristic of component in the sample, and the intensity transmitted through each filter represents a direct measure of the associated score values. Essentially, compressive hyperspectral images consist of ‘score’ pixels (instead of ‘spectral’ pixels). This paper presents an overview of recent advances in compressive Raman detection designs and performance validations using a DMD based binary detection strategy. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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