Special Issue "Hyper- and Multi-Spectral Imaging"

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

Deadline for manuscript submissions: closed (30 June 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Costas Balas

Department of Electronic and Computer Engineering, Technical University of Crete, Chania 73100, Greece
Website | E-Mail
Interests: hyper-multi-spectral imaging; chemical imaging; optical spectroscopy; mid-IR imaging spectroscopy; biophotonics; biomedical optical imaging; dynamic contrast enhanced bio-imaging; fluorescence microscopy; optical diagnosis of neoplasia; in silico modeling of bio-optical processes; biomedical device instrumentation; imaging systems and methods for nondestructive analysis

Special Issue Information

Dear Colleagues,

Spectral Imaging (SI) combines the advantages of both imaging and spectroscopy (high spatial and spectral resolution) in a single instrument. In SI, light intensity is recorded as a function of both wavelength and location. The output is a three-dimensional data structure known as spectral cube, with each pixel representing the spectrum of the scene at that point.

Most recent developments include snapshot or single exposure SI cameras, which capture the images of the spectral cube simultaneously or, alternatively, spectral cube streams at nearly video rates. Dynamic SI implies that light intensity can now be recorded as a function of time, wavelength, polarization, two or more spatial locations, etc.

Adding new dimensions to the data structure is motivated by the steep expansion of SI applications, which are increasingly migrating from defense/satellite domain towards prevalently civilian uses. On the other hand, and for the purpose of handling the generated massive data volume, SI motivates the development of advanced and fast classification, spectral unmixing and data reduction algorithms, spectral class visualization techniques, etc. SI is rapidly developing because numerus diverse disciplines have joined efforts towards further advancing technologies and expanding applications. Opto-and micro-electronics, computation imaging, analytical sciences, remote sensing, non-destructive testing, biomedical imaging are the disciplines that have been instrumental to these developments.

We invite investigators to contribute original research articles, as well as review articles, that will stimulate the continuing efforts in the field of SI.

Potential topics include, but are not limited to:

  • Snapshot or scanning spectral imaging camera systems
  • SI-related computational imaging, machine learning, data mining, spectral classification, spectral unmixing, data reduction
  • Applications

Prof. Costas Balas
Guest Editor

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Keywords

  • Hyper-Spectral Imaging
  • Multi-Spectral Imaging
  • Snap-Shot Spectral Imaging
  • Spectral Cube Data Analysis/Processing
  • Biomedicine
  • Remote sensing
  • Microscopy
  • Non-Destructive Testing

Published Papers (4 papers)

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Research

Open AccessArticle Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy
Appl. Sci. 2018, 8(9), 1435; https://doi.org/10.3390/app8091435
Received: 6 July 2018 / Revised: 19 August 2018 / Accepted: 20 August 2018 / Published: 22 August 2018
Cited by 2 | PDF Full-text (5702 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the [...] Read more.
Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR705), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60°. Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution. Full article
(This article belongs to the Special Issue Hyper- and Multi-Spectral Imaging)
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Open AccessArticle Parallel Crossed Chaotic Encryption for Hyperspectral Images
Appl. Sci. 2018, 8(7), 1183; https://doi.org/10.3390/app8071183
Received: 29 June 2018 / Revised: 16 July 2018 / Accepted: 17 July 2018 / Published: 20 July 2018
PDF Full-text (2237 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral images (HI) collect information from across the electromagnetic spectrum, and they are an essential tool for identifying materials, recognizing processes and finding objects. However, the information on an HI could be sensitive and must to be protected. Although there are many encryption [...] Read more.
Hyperspectral images (HI) collect information from across the electromagnetic spectrum, and they are an essential tool for identifying materials, recognizing processes and finding objects. However, the information on an HI could be sensitive and must to be protected. Although there are many encryption schemes for images and raw data, there are not specific schemes for HI. In this paper, we introduce the idea of crossed chaotic systems and we present an ad hoc parallel crossed chaotic encryption algorithm for HI, in which we take advantage of the multidimensionality nature of the HI. Consequently, we obtain a faster encryption algorithm and with a higher entropy result than others state of the art chaotic schemes. Full article
(This article belongs to the Special Issue Hyper- and Multi-Spectral Imaging)
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Open AccessArticle Measuring Identification and Quantification Errors in Spectral CT Material Decomposition
Appl. Sci. 2018, 8(3), 467; https://doi.org/10.3390/app8030467
Received: 4 February 2018 / Revised: 13 March 2018 / Accepted: 16 March 2018 / Published: 18 March 2018
Cited by 1 | PDF Full-text (7683 KB) | HTML Full-text | XML Full-text
Abstract
Material decomposition methods are used to identify and quantify multiple tissue components in spectral CT but there is no published method to quantify the misidentification of materials. This paper describes a new method for assessing misidentification and mis-quantification in spectral CT. We scanned [...] Read more.
Material decomposition methods are used to identify and quantify multiple tissue components in spectral CT but there is no published method to quantify the misidentification of materials. This paper describes a new method for assessing misidentification and mis-quantification in spectral CT. We scanned a phantom containing gadolinium (1, 2, 4, 8 mg/mL), hydroxyapatite (54.3, 211.7, 808.5 mg/mL), water and vegetable oil using a MARS spectral scanner equipped with a poly-energetic X-ray source operated at 118 kVp and a CdTe Medipix3RX camera. Two imaging protocols were used; both with and without 0.375 mm external brass filter. A proprietary material decomposition method identified voxels as gadolinium, hydroxyapatite, lipid or water. Sensitivity and specificity information was used to evaluate material misidentification. Biological samples were also scanned. There were marked differences in identification and quantification between the two protocols even though spectral and linear correlation of gadolinium and hydroxyapatite in the reconstructed images was high and no qualitative segmentation differences in the material decomposed images were observed. At 8 mg/mL, gadolinium was correctly identified for both protocols, but concentration was underestimated by over half for the unfiltered protocol. At 1 mg/mL, gadolinium was misidentified in 38% of voxels for the filtered protocol and 58% of voxels for the unfiltered protocol. Hydroxyapatite was correctly identified at the two higher concentrations for both protocols, but mis-quantified for the unfiltered protocol. Gadolinium concentration as measured in the biological specimen showed a two-fold difference between protocols. In future, this methodology could be used to compare and optimize scanning protocols, image reconstruction methods, and methods for material differentiation in spectral CT. Full article
(This article belongs to the Special Issue Hyper- and Multi-Spectral Imaging)
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Open AccessArticle A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
Appl. Sci. 2018, 8(3), 431; https://doi.org/10.3390/app8030431
Received: 20 February 2018 / Revised: 6 March 2018 / Accepted: 9 March 2018 / Published: 13 March 2018
Cited by 4 | PDF Full-text (8627 KB) | HTML Full-text | XML Full-text
Abstract
Raman spectral imaging is an effective method to analyze and evaluate the chemical composition and structure of a sample, and has many applications for food safety and quality research. This study developed a 1064 nm dispersive Raman spectral imaging system for surface and [...] Read more.
Raman spectral imaging is an effective method to analyze and evaluate the chemical composition and structure of a sample, and has many applications for food safety and quality research. This study developed a 1064 nm dispersive Raman spectral imaging system for surface and subsurface analysis of food samples. A 1064 nm laser module is used for sample excitation. A bifurcated optical fiber coupled with Raman probe is used to focus excitation laser on the sample and carry scattering signal to the spectrograph. A high throughput volume phase grating disperses the incoming Raman signal. A 512 pixels Indium-Gallium-Arsenide (InGaAs) detector receives the dispersed light signal. A motorized positioning table moves the sample in two-axis directions, accumulating hyperspectral image of the sample by the point-scan method. An interface software was developed in-house for parameterization, data acquisition, and data transfer. The system was spectrally calibrated using naphthalene and polystyrene. It has the Raman shift range of 142 to 1820 cm−1, the spectral resolution of 12 cm−1 at full width half maximum (FWHM). The spatial resolution of the system was evaluated using a standard resolution glass test chart. It has the spatial resolution of 0.1 mm. The application of the system was demonstrated by surface and subsurface detection of metanil yellow contamination in turmeric powder. Results indicate that the 1064 nm dispersive Raman spectral imaging system is a useful tool for food safety and quality evaluation. Full article
(This article belongs to the Special Issue Hyper- and Multi-Spectral Imaging)
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