Journal articles: 'Hyperspectral imaging spectroscopy'

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Vohland, Michael, and András Jung. "Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences." Remote Sensing 12, no. 18 (September 11, 2020): 2962. http://dx.doi.org/10.3390/rs12182962.

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Si Fu-Qi, Xie Pin-Hua, Klaus-Peter Heue, Liu-Cheng, Peng Fu-Min, and Liu Wen-Qing. "Hyperspectral imaging differential optical absorption spectroscopy." Acta Physica Sinica 57, no. 9 (2008): 6018. http://dx.doi.org/10.7498/aps.57.6018.

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Fickus, Matthew, Megan E. Lewis, Dustin G. Mixon, and Jesse Peterson. "Compressive Hyperspectral Imaging for Stellar Spectroscopy." IEEE Signal Processing Letters 22, no. 11 (November 2015): 1829–33. http://dx.doi.org/10.1109/lsp.2015.2433837.

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Zhang, Jun, Zihao Liu, Yaoyuan Pu, Jiajun Wang, Binman Tang, Limin Dai, Shuihua Yu, and Ruqing Chen. "Identification of Transgenic Agricultural Products and Foods Using NIR Spectroscopy and Hyperspectral Imaging: A Review." Processes 11, no. 3 (February 21, 2023): 651. http://dx.doi.org/10.3390/pr11030651.

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Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.

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Diezma, B., S. Franco, L. Lleó, T. Presečki, and J. M. Roger. "Grading banana by VNIR hyperspectral imaging spectroscopy." Acta Horticulturae, no. 1194 (March 2018): 1283–90. http://dx.doi.org/10.17660/actahortic.2018.1194.181.

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Hanuš, J., T. Fabiánek, and L. Fajmon. "POTENTIAL OF AIRBORNE IMAGING SPECTROSCOPY AT CZECHGLOBE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 15–17. http://dx.doi.org/10.5194/isprsarchives-xli-b1-15-2016.

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Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all sensors. The new hyperspectral imaging system required adaptations in the data pre-processing chain. The established pre-processing chain (radiometric, atmospheric and geometric corrections), which was tailored mainly to the AISA Eagle instrument operated at CzechGlobe since 2004, has been now modified to fit the new system and users needs. Continuous development of the processing chain is now focused mainly on establishing pre-processing of thermal data including emissivity estimation and also on joint processing of hyperspectral and laser scanning data.

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Hanuš, J., T. Fabiánek, and L. Fajmon. "POTENTIAL OF AIRBORNE IMAGING SPECTROSCOPY AT CZECHGLOBE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 15–17. http://dx.doi.org/10.5194/isprs-archives-xli-b1-15-2016.

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Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all sensors. The new hyperspectral imaging system required adaptations in the data pre-processing chain. The established pre-processing chain (radiometric, atmospheric and geometric corrections), which was tailored mainly to the AISA Eagle instrument operated at CzechGlobe since 2004, has been now modified to fit the new system and users needs. Continuous development of the processing chain is now focused mainly on establishing pre-processing of thermal data including emissivity estimation and also on joint processing of hyperspectral and laser scanning data.

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Kashani, Amir H., Mark Wong, Nicole Koulisis, Chein-I. Chang, Gabriel Martin, and Mark S. Humayun. "Hyperspectral imaging of retinal microvascular anatomy." Journal of Biomedical Engineering and Informatics 2, no. 1 (November 22, 2015): 139. http://dx.doi.org/10.5430/jbei.v2n1p139.

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Background: Hyperspectral image processing has been applied to many aspects of astronomical and earth science research. Furthermore, advances in computed tomographic imaging spectroscopy and diffraction grating design have allowed biological applications for non-invasive tissue analysis. Herein, we describe a hyperspectral computed tomographic imaging spectroscope (HCTIS) that provides high spatial, spectral and temporal resolution ideal for imaging biological tissue in vivo. Methods: We demonstrate proof-of-principle application of the HCTIS by imaging and mapping the microvascular anatomy of the retina of a model organism (rabbit) in vivo. The imaging procedure allows rapid and dense spectral sampling, is non-toxic, non-invasive, and easily adaptable to a commercially available fundus camera system. Results: HCTIS provides highly co-registered temporal, spatial and spectral data with resolution capable of reconstructing the fine vascular tree of the rabbit retina in vivo. Conclusions: We show that HCTIS allows for reliable and reproducible tissue classification and detection using signature discriminant analysis. Future applications of this system may provide promising diagnostic methods for diseases of many tissues.

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Pallua, Johannes D., Andrea Brunner, Bernhard Zelger, Christian W. Huck, Michael Schirmer, Johannes Laimer, David Putzer, Martin Thaler, and Bettina Zelger. "New perspectives of hyperspectral imaging for clinical research." NIR news 32, no. 3-4 (June 2021): 5–13. http://dx.doi.org/10.1177/09603360211024971.

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New developments in instrumentation and data analysis have further improved the perspectives of hyperspectral imaging in clinical use. Thus, hyperspectral imaging can be considered as “Next Generation Imaging” for future clinical research. As a contactless, non-invasive method with short process times of just a few seconds, it quantifies predefined substance classes. Results of hyperspectral imaging may support the detection of carcinomas and the classification of different tissue structures as well as the assessment of tissue blood flow. Taken together, this method combines the principle of spectroscopy with imaging using conventional visual cameras. Compared to other optical imaging methods, hyperspectral imaging also analyses deeper layers of tissue.

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Lawrence, Kurt C., William R. Windham, Bosoon Park, and R. Jeff Buhr. "A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses." Journal of Near Infrared Spectroscopy 11, no. 4 (August 2003): 269–81. http://dx.doi.org/10.1255/jnirs.373.

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A method and system for detecting faecal and ingesta contaminants on poultry carcasses were demonstrated. A visible/near infrared monochromator, which measured reflectance and principal component analysis were first used to identify key wavelengths from faecal and uncontaminated skin samples. Measurements at 434, 517, 565 and 628 nm were identified and used for evaluation with a hyperspectral imaging system. The hyperspectral imaging system, which was a line-scan (pushbroom) imaging system, consisted of a hyperspectral camera, fibre-optic line lights, a computer and frame grabber. The hyperspectral imaging camera consisted of a high-resolution charge coupled device (CCD) camera, a prism-grating-prism spectrograph, focusing lens, associated optical hardware and a motorised controller. The imaging system operated from about 400 to 900 nm. The hyperspectral imaging system was calibrated for wavelength, distance and percent reflectance and analysis of calibrated images at the key wavelengths indicated that single-wavelength images were inadequate for detecting contaminants. However, a ratio of images at two of the key wavelengths was able to identify faecal and ingesta contaminants. Specifically, the ratio of the 565-nm image divided by the 517-nm image produced good results. The ratio image was then further processed by masking the background and either enhancing the image contrast with a non-linear histogram stretch, or applying a faecal threshold. The results indicated that, for the limited sample population, more than 96% of the contaminants were detected. Thus, the hyperspectral imaging system was able to detect contaminants and showed feasibility, but was too slow for real-time on-line processing. Therefore, a multivariate system operating at 565 and 517 nm, which should be capable of operating at real-time on-line processing speed, should be used. Further research with such a system needs to be conducted.

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Jiang, Hongzhe, Wei Wang, Xinzhi Ni, Hong Zhuang, Seung-Chul Yoon, and Kurt C. Lawrence. "Recent advancement in near infrared spectroscopy and hyperspectral imaging techniques for quality and safety assessment of agricultural and food products in the China Agricultural University." NIR news 29, no. 8 (October 1, 2018): 19–23. http://dx.doi.org/10.1177/0960336018804755.

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Near infrared spectroscopy and hyperspectral imaging are fast-growing, rapid, powerful, and non-destructive optical technologies that can be used especially in quality and safety control of agro-food products. The Non-destructive Detecting Laboratory for Agricultural and Food Products in the College of Engineering, China Agricultural University in Beijing, China, has engaged in research on sensing and characterizing agro-food quality and safety attributes with the latest optical methods including near infrared spectroscopy and hyperspectral imaging for over five years. In this report, some of our latest research and developments through multidisciplinary international collaborations will be highlighted to demonstrate our contributions to this near infrared spectroscopy and hyperspectral imaging sensing area to improve non-destructive diagnosis and quality control of agricultural and food products.

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Juntunen, Cory, Isabel M. Woller, and Yongjin Sung. "Hyperspectral Three-Dimensional Fluorescence Imaging Using Snapshot Optical Tomography." Sensors 21, no. 11 (May 24, 2021): 3652. http://dx.doi.org/10.3390/s21113652.

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Hyperspectral three-dimensional (3D) imaging can provide both 3D structural and functional information of a specimen. The imaging throughput is typically very low due to the requirement of scanning mechanisms for different depths and wavelengths. Here we demonstrate hyperspectral 3D imaging using Snapshot projection optical tomography (SPOT) and Fourier-transform spectroscopy (FTS). SPOT allows us to instantaneously acquire the projection images corresponding to different viewing angles, while FTS allows us to perform hyperspectral imaging at high spectral resolution. Using fluorescent beads and sunflower pollens, we demonstrate the imaging performance of the developed system.

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Jiang, Ying Lan, Ruo Yu Zhang, Jie Yu, Wan Chao Hu, and Zhang Tao Yin. "Applications of Visible and near-Infrared Hyperspectral Imaging for Non-Destructive Detection of the Agricultural Products." Advanced Materials Research 317-319 (August 2011): 909–14. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.909.

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Agricultural products quality which included intrinsic attribute and extrinsic characteristic, closely related to the health of consumer and the exported cost. Now, imaging (machine vision) and spectrum are two main nondestructive inspection technologies to be applied. Hyperspectral imaging, a new emerging technology developed for detecting quality of the food and agricultural products in recent years, combined techniques of conventional imaging and spectroscopy to obtain both spatial and spectral information from an objective simultaneously. This paper compared the advantage and disadvantage of imaging, spectrum and hyperspectral imaging technique, and provided a description to basic principle, feature of hyperspectral imaging system and calibration of hyperspectral reflectance images. In addition, the recent advances for the application of hyperspectral imaging to agricultural products quality inspection were reviewed in other countries and China.

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Al Ktash, Mohammad, Mona Stefanakis, Tim Englert, Maryam S. L. Drechsel, Jan Stiedl, Simon Green, Timo Jacob, et al. "UV Hyperspectral Imaging as Process Analytical Tool for the Characterization of Oxide Layers and Copper States on Direct Bonded Copper." Sensors 21, no. 21 (November 4, 2021): 7332. http://dx.doi.org/10.3390/s21217332.

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Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R2c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes.

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Shukla, Alpana, and Rajsi Kot. "An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines." IRA-International Journal of Applied Sciences (ISSN 2455-4499) 5, no. 2 (December 12, 2016): 85. http://dx.doi.org/10.21013/jas.v5.n2.p4.

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<div>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background. Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</div>

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Baltsavias, Emmanuel P. "Special section on Image Spectroscopy and Hyperspectral Imaging." ISPRS Journal of Photogrammetry and Remote Sensing 57, no. 3 (December 2002): 169–70. http://dx.doi.org/10.1016/s0924-2716(02)00127-2.

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Askari, Mohammad Sadegh, Sharon M. O'Rourke, and Nicholas M. Holden. "A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon." Journal of Near Infrared Spectroscopy 26, no. 2 (April 2018): 133–46. http://dx.doi.org/10.1177/0967033518766668.

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This study evaluated whether the accuracy of soil organic carbon measurement by laboratory hyperspectral imaging can match that of standard point spectroscopy operating in the visible–near infrared. Hyperspectral imaging allows a greater amount of spectral information to be collected from the soil sample compared to standard spectroscopy, accounting for greater sample representation. A total of 375 representative Irish soils were scanned by two-point spectrometers (a Foss NIR Systems 6500 labelled S-1 and a Varian FT-IR 3100 labelled S-2) and two laboratory hyperspectral imaging systems (two push broom line-scanning hyperspectral imaging systems manufactured by DV optics and Spectral Imaging Ltd, respectively, labelled S-3 and S-4). The objectives were (a) to compare the predictive ability of spectral datasets for soil organic carbon prediction for each instrument evaluated and (b) to assess the impact of imposing a common wavelength range and spectral resolution on soil organic carbon model accuracy. These objectives examined the predictive ability of spectral datasets for soil organic carbon prediction based on optimal settings of each instrument in (a) and introduced a constraint in wavelength range and spectral resolution to achieve common settings for instruments in (b). Based on optimal settings for each instrument, the deviation (root-mean square error of prediction) from the best fit line between laboratory measured and predicted soil organic carbon, ranked the instruments as S-1 (26.3 g kg−1) < S-2 (29.4 g kg−1) < S-3 (34.3 g kg−1) < S-4 (41.1 g kg−1). The S-1 model outperformed in all partial least squares regression performance indicators, and across all spectral ranges, and produced the most favourable outcomes in means testing, variance testing and identification of significant variables. It is assumed that a larger wavelength range produced more accurate soil organic carbon predictions for S-1 and S-2. Under common instrument settings, the prediction accuracy for S-3 that was almost equal to S-1. It is concluded that under standard operating procedures, greater soil sample representation captured by hyperspectral imaging can equal the quality of the spectra from point spectroscopy. This result is important for the development of laboratory hyperspectral imaging for soil image analysis.

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Cebeci, Derya, Bharat Mankani, and Dor Ben-Amotz. "Recent Trends in Compressive Raman Spectroscopy Using DMD-Based Binary Detection." Journal of Imaging 5, no. 1 (December 21, 2018): 1. http://dx.doi.org/10.3390/jimaging5010001.

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The collection of high-dimensional hyperspectral data is often the slowest step in the process of hyperspectral Raman imaging. Where 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. This is 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.

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Caldwell, Joshua D., Laurent Lombez, Amaury Delamarre, Jean Francois Guillemoles, Brice Bourgoin, Brett A. Hull, and Marc Verhaegen. "Luminescence Imaging of Extended Defects in SiC via Hyperspectral Imaging." Materials Science Forum 717-720 (May 2012): 403–6. http://dx.doi.org/10.4028/www.scientific.net/msf.717-720.403.

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Over the past decade, improvements in silicon carbide growth and materials has led to the development of commercialized unipolar devices such as Schottky diodes and MOSFETs, however, much work remains to realizing the goal of wide-scale commercialization of both unipolar and bipolar devices such as pin diodes or IGBTs, for high applications requiring high powers, operating in elevated temperatures or radiation environments or for many fast switching applications. Despite the great strides that have been made in reducing extended and point defect densities during this period, such defects still remain and with the push to lower off-cut angle substrates are in many cases seeing increases in prevalence. Thus, spectroscopic and imaging techniques for locating and identifying these defects are in high demand. Luminescence imaging and spectroscopy have both been utilized heavily in such work, yet simultaneously obtaining corresponding spectroscopic and spatial information from such defects is problematic. Here we report on hyperspectral imaging of electroluminescence from SiC pin diodes, whereby a stack of luminescence images are collected over a wide spectral range (400-900 nm), thereby providing the ability to both image distinct features and identify their corresponding spectral properties. This process is also equally applicable to collecting either photo- or electroluminescence from other materials or devices emitting in either the UV-Vis or NIR spectral range, as well as to reflectance, transmission or other imaging techniques.

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Christensen, Kenneth A., and Michael D. Morris. "Hyperspectral Raman Microscopic Imaging Using Powell Lens Line Illumination." Applied Spectroscopy 52, no. 9 (September 1998): 1145–47. http://dx.doi.org/10.1366/0003702981945138.

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The design and characterization of a simple and robust hyperspectral Raman line imaging illumination system with the use of a Powell lens is reported. The generated line uniformity is ±5% of total intensity with a laser power density of 12 mW/μm2 at the sample with a 50×/0.8 NA (numerical aperture) objective. Similar results were obtained by using other objectives. Linewidths remained near the diffraction limit for all objectives tested. Significant decreases in image acquisition time are also reported with the use of a Powell lens-illuminated hyperspectral Raman line imaging microscope equipped with an intensified charge-coupled device (CCD) detector. Hyperspectral images (100 × 350 pixels) were acquired in as little as 8 with a corresponding signal-to-noise ratio of 24.

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Wu, Taixia, Guanghua Li, Zehua Yang, Hongming Zhang, Yong Lei, Nan Wang, and Lifu Zhang. "Shortwave Infrared Imaging Spectroscopy for Analysis of Ancient Paintings." Applied Spectroscopy 71, no. 5 (November 24, 2016): 977–87. http://dx.doi.org/10.1177/0003702816660724.

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Spectral analysis is one of the main non-destructive techniques used to examine cultural relics. Hyperspectral imaging technology, especially on the shortwave infrared (SWIR) band, can clearly extract information from paintings, such as color, pigment composition, damage characteristics, and painting techniques. All of these characteristics have significant scientific and practical value in the study of ancient paintings and other relics and in their protection and restoration. In this study, an ancient painting, numbered Gu-6541, which had been found in the Forbidden City, served as a sample. A ground-based SWIR imaging spectrometer was used to produce hyperspectral images with high spatial and spectral resolution. Results indicated that SWIR imaging spectral data greatly facilitates the extraction of line features used in drafting, even using a single band image. It can be used to identify and classify mineral pigments used in paintings. These images can detect alterations and traces of daub used in painting corrections and, combined with hyperspectral data analysis methods such as band combination or principal component analysis, such information can be extracted to highlight outcomes of interest. In brief, the SWIR imaging spectral technique was found to have a highly favorable effect on the extraction of line features from drawings and on the identification of colors, classification of paintings, and extraction of hidden information.

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Lu, Yuzhen, and Renfu Lu. "Non-Destructive Defect Detection of Apples by Spectroscopic and Imaging Technologies: A Review." Transactions of the ASABE 60, no. 5 (2017): 1765–90. http://dx.doi.org/10.13031/trans.12431.

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Abstract. Apples are susceptible to a wide range of defects that can occur in the orchard and during the post-harvest period. Detection of these defects by non-destructive sensing techniques is of great importance for the apple industry and has been an intensive research topic over the past two decades. This review presents an overview of common defects in apples, encompassing physiological disorders, mechanical damage, pathological disorders, and contamination. Presented and discussed in this review is research progress on the detection of defects in apples using various non-destructive spectroscopic and imaging techniques, including visible/near-infrared spectroscopy, fluorescence spectroscopy and imaging, monochromatic and color imaging, hyperspectral and multispectral imaging, x-ray imaging, magnetic resonance imaging, thermal imaging, time-resolved and spatially resolved spectroscopy, Raman spectroscopy, biospeckle imaging, and structured-illumination reflectance imaging. This review concludes with remarks on the prospects of these techniques and research needs in the future. Keywords: Apples, Defects, Imaging, Non-destructive detection, Quality, Safety, Spectroscopy.

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Berisha, Sebastian, Shengyuan Chang, Sam Saki, Davar Daeinejad, Ziqi He, Rupali Mankar, and David Mayerich. "SIproc: an open-source biomedical data processing platform for large hyperspectral images." Analyst 142, no. 8 (2017): 1350–57. http://dx.doi.org/10.1039/c6an02082h.

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Manley, Marena. "Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials." Chem. Soc. Rev. 43, no. 24 (2014): 8200–8214. http://dx.doi.org/10.1039/c4cs00062e.

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Anthony, Stephen M., and Jerilyn A. Timlin. "Removing Cosmic Spikes Using a Hyperspectral Upper-Bound Spectrum Method." Applied Spectroscopy 71, no. 3 (November 5, 2016): 507–19. http://dx.doi.org/10.1177/0003702816668528.

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Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.

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Picollo, Marcello, Costanza Cucci, Andrea Casini, and Lorenzo Stefani. "Hyper-Spectral Imaging Technique in the Cultural Heritage Field: New Possible Scenarios." Sensors 20, no. 10 (May 16, 2020): 2843. http://dx.doi.org/10.3390/s20102843.

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Imaging spectroscopy technique was introduced in the cultural heritage field in the 1990s, when a multi-spectral imaging system based on a Vidicon camera was used to identify and map pigments in paintings. Since then, with continuous improvements in imaging technology, the quality of spectroscopic information in the acquired imaging data has greatly increased. Moreover, with the progressive transition from multispectral to hyperspectral imaging techniques, numerous new applicative perspectives have become possible, ranging from non-invasive monitoring to high-quality documentation, such as mapping and characterization of polychrome and multi-material surfaces of cultural properties. This article provides a brief overview of recent developments in the rapidly evolving applications of hyperspectral imaging in this field. The fundamentals of the various strategies, that have been developed for applying this technique to different types of artworks are discussed, together with some examples of recent applications.

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Selci, Stefano. "The Future of Hyperspectral Imaging." Journal of Imaging 5, no. 11 (October 25, 2019): 84. http://dx.doi.org/10.3390/jimaging5110084.

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The Special Issue on hyperspectral imaging (HSI), entitled “The Future of Hyperspectral Imaging”, has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI. Some contributions also update specific methodological or mathematical tools. In particular, the review papers address deep learning methods for HSI analysis, while HSI data compression is reviewed by using liquid crystals spectral multiplexing as well as DMD-based Raman spectroscopy. Specific topics explored by using data obtained by HSI include alert on the sprouting of potato tubers, the investigation on the stability of painting samples, the prediction of healing diabetic foot ulcers, and age determination of blood-stained fingerprints. Papers showing advances on more general topics include video approach for HSI dynamic scenes, localization of plant diseases, new methods for the lossless compression of HSI data, the fusing of multiple multiband images, and mixed modes of laser HSI imaging for sorting and quality controls.

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Tappy, Nicolas, Anna Fontcuberta i Morral, and Christian Monachon. "Image shift correction, noise analysis, and model fitting of (cathodo-)luminescence hyperspectral maps." Review of Scientific Instruments 93, no. 5 (May 1, 2022): 053702. http://dx.doi.org/10.1063/5.0080486.

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Hyperspectral imaging is an important asset of modern spectroscopy. It allows us to perform optical metrology at a high spatial resolution, for example in cathodoluminescence in scanning electron microscopy. However, hyperspectral datasets present added challenges in their analysis compared to individually taken spectra due to their lower signal to noise ratio and specific aberrations. On the other hand, the large volume of information in a hyperspectral dataset allows the application of advanced statistical analysis methods derived from machine-learning. In this article, we present a methodology to perform model fitting on hyperspectral maps, leveraging principal component analysis to perform a thorough noise analysis of the dataset. We explain how to correct the imaging shift artifact, specific to imaging spectroscopy, by directly evaluating it from the data. The impact of goodness-of-fit-indicators and parameter uncertainties is discussed. We provide indications on how to apply this technique to a variety of hyperspectral datasets acquired using other experimental techniques. As a practical example, we provide an implementation of this analysis using the open-source Python library hyperspy, which is implemented using the well established Jupyter Notebook framework in the scientific community.

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Fernandes, Armando, José Lousada, José Morais, José Xavier, João Pereira, and Pedro Melo-Pinto. "Measurement of intra-ring wood density by means of imaging VIS/NIR spectroscopy (hyperspectral imaging)." Holzforschung 67, no. 1 (January 1, 2013): 59–65. http://dx.doi.org/10.1515/hf-2011-0258.

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Abstract This paper reports a novel application of hyperspectral imaging (a spectroscopic technique) for measuring wood density profiles at the growth ring scale. The measurements were performed with a spatial resolution of 79 µm. In the present case, hyperspectral imaging was used to measure wood sample reflectance for light in the wavelength range between 380 and 1028 nm, with a resolution of approximately 0.6 nm. The work was performed with 34 samples collected from 34 trees of Pinus pinea. A total of 34,093 density points were used to create and validate a partial least-squares (PLS) regression that converts spectroscopic reflectance data into density values. The coefficient of determination value between the present method and X-ray microdensitometry is 0.810 with a root mean squared error of 6.54×10-2 g.cm-3.

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Mierczyk, Monika, Bogdan Zagajewski, Anna Jarocińska, and Roksana Knapik. "Assessment of Imaging Spectroscopy for rock identification in the Karkonosze Mountains, Poland." Miscellanea Geographica 20, no. 1 (March 1, 2016): 34–40. http://dx.doi.org/10.1515/mgrsd-2015-0016.

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Abstract Based on laboratory, field and airborne-acquired hyperspectral data, this paper aims to analyse the dominant minerals and rocks found in the Polish Karkonosze Mountains. Laboratory spectral characteristics were measured with the ASD FieldSpec 3 spectrometer and images were obtained from VITO’s Airborne Prism EXperiment (APEX) scanner. The terrain is covered mainly by lichens or vascular plants creating significant difficulties for rock identification. However, hyperspectral airborne imagery allowed for subpixel classifications of different types of granites, hornfels and mica schist within the research area. The hyperspectral data enabled geological mapping of bare ground that had been masked out using three advanced algorithms: Spectral Angle Mapper, Linear Spectral Unmixing and Matched Filtering. Though all three methods produced positive results, the Matched Filtering method proved to be the most effective. The result of this study was a set of maps and post classification statistical data of rock distribution in the area, one for each method of classification.

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Nogo, Kosuke, Kou Ikejima, Wei Qi, Natsumi Kawashima, Tomoya Kitazaki, Satoru Adachi, Kenji Wada, Akira Nishiyama, and Ichiro Ishimaru. "Identification of black microplastics using long-wavelength infrared hyperspectral imaging with imaging-type two-dimensional Fourier spectroscopy." Analytical Methods 13, no. 5 (2021): 647–59. http://dx.doi.org/10.1039/d0ay01738h.

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Maraphum, Kanvisit, Khwantri Saengprachatanarug, Kittipon Aparatana, Yoshinari Izumikawa, and Eizo Taira. "Spatial mapping of Brix and moisture content in sugarcane stalk using hyperspectral imaging." Journal of Near Infrared Spectroscopy 28, no. 4 (February 27, 2020): 167–74. http://dx.doi.org/10.1177/0967033520905370.

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Hyperspectral imaging is a powerful technique that can rapidly, accurately, and non-destructively determine the quality of agricultural products. In this study, a hyperspectral imaging system has been developed to evaluate and visualize the Brix values and moisture contents in sugarcane stalks to be used as a tool for breeding programmes. After extracting the spectral data via ENVI coding, data in the wavelength range of 450–950 nm were used to generate prediction models for Brix and moisture content via partial least squares regression. The coefficients of determination of the predictive models for Brix and moisture content were found to be 0.70 and 0.68, respectively. The root mean square errors of cross-validation were 1.28° for Brix and 1.49% for moisture content, and the performance to deviation ratios were 1.71 and 1.61, respectively. The models were applied to each pixel of the hypercube data in order to determine the distributions of Brix and moisture content within the sugarcane stalks. Both distribution mappings indicated that the Brix and the moisture content level were lower in the internode regions. The results demonstrated the feasibility of using hyperspectral imaging to visualize Brix and moisture content in sugarcane stalks. The developed method has potential applications in farming management and also in breeding programs.

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Villegas, Arturo, and Juan P. Torres. "Spatial spectroscopy for high resolution imaging." EPJ Web of Conferences 238 (2020): 06005. http://dx.doi.org/10.1051/epjconf/202023806005.

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Quantum estimation theory provides bounds for the precision in the estimation of a set of parameters that characterize a system. Two questions naturally arise: Is any of these bounds tight? And if this is the case, what type of measurements can attain such a limit? In this work we show that for phase objects, it is possible to find a tight resolution bound. Moreover one can find a set of spatial modes whose detection provides an optimal estimation of the complete set of parameters for which we propose a homodyne detection scheme. We call this method spatial spectroscopy since it mimics in the spatial domain what conventional spectroscopy methods do in the frequency domain employing many frequencies (hyperspectral imaging).

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Chaudhary, Siddharth, Sarawut Ninsawat, and Tai Nakamura. "Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System." Sensors 19, no. 1 (December 28, 2018): 97. http://dx.doi.org/10.3390/s19010097.

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The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.

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Crocombe, Richard A. "Portable Spectroscopy." Applied Spectroscopy 72, no. 12 (October 18, 2018): 1701–51. http://dx.doi.org/10.1177/0003702818809719.

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Until very recently, handheld spectrometers were the domain of major analytical and security instrument companies, with turnkey analyzers using spectroscopic techniques from X-ray fluorescence (XRF) for elemental analysis (metals), to Raman, mid-infrared, and near-infrared (NIR) for molecular analysis (mostly organics). However, the past few years have seen rapid changes in this landscape with the introduction of handheld laser-induced breakdown spectroscopy (LIBS), smartphone spectroscopy focusing on medical diagnostics for low-resource areas, commercial engines that a variety of companies can build up into products, hyphenated or dual technology instruments, low-cost visible-shortwave NIR instruments selling directly to the public, and, most recently, portable hyperspectral imaging instruments. Successful handheld instruments are designed to give answers to non-scientist operators; therefore, their developers have put extensive resources into reliable identification algorithms, spectroscopic libraries or databases, and qualitative and quantitative calibrations. As spectroscopic instruments become smaller and lower cost, “engines” have emerged, leading to the possibility of being incorporated in consumer devices and smart appliances, part of the Internet of Things (IOT). This review outlines the technologies used in portable spectroscopy, discusses their applications, both qualitative and quantitative, and how instrument developers and vendors have approached giving actionable answers to non-scientists. It outlines concerns on crowdsourced data, especially for heterogeneous samples, and finally looks towards the future in areas like IOT, emerging technologies for instruments, and portable hyphenated and hyperspectral instruments.

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Juliano da Silva, Carlos, and Celio Pasquini. "Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber." Analyst 140, no. 2 (2015): 512–22. http://dx.doi.org/10.1039/c4an00836g.

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Jestel, Nancy L., Jeremy M. Shaver, and Michael D. Morris. "Hyperspectral Raman Line Imaging of an Aluminosilicate Glass." Applied Spectroscopy 52, no. 1 (January 1998): 64–69. http://dx.doi.org/10.1366/0003702981942339.

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An aluminosilicate glass, which is a model for glass formulations used as dental restorations, was examined by hyperspectral Raman line imaging. The data set consisted of more than 30000 spectra, which were analyzed by using factor analysis. Nine score images were constructed from the nine significant factors identified. Three factors represent convolutions of noise, background, and offset. The other six factors represent Raman spectra of different bonding environments of the silicate tetrahedron. Three of those factors contain narrow Raman features. These are associated with a fully polymerized silica network, with a silicate tetrahedron with one nonbridging oxygen, and with an alumina-related inclusion or a silicate tetrahedron with two nonbridging oxygens. The last three significant factors contain broad Raman bands representing continua of slightly different bonding environments of silicate tetrahedra with 0–4 nonbridging oxygens. The score images reveal that the glass, although not homogeneous, has few regions with discrete heterogeneities. The different bonding networks commingle and could be interconnected.

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Zhang, Shuliang L., Jerilyn A. Pezzuti, Michael D. Morris, Aruna Appadwedula, Chang-Meng Hsiung, M. Anne Leugers, and David Bank. "Hyperspectral Raman Line Imaging of Syndiotactic Polystyrene Crystallinity." Applied Spectroscopy 52, no. 10 (October 1998): 1264–68. http://dx.doi.org/10.1366/0003702981942753.

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The crystallinity of syndiotactic polystyrene (sPS) is studied by hyperspectral Raman line imaging. Images of a 140 × 1200 μm region of an sPS test piece containing 39 200 pixels/image were generated from spectra taken over the wavenumber interval between 300 and 875 cm−1. The spectral region includes the moderate-intensity crystallinity-sensitive bands in the 770 800 cm−1 region, as well as other useful but weaker marker bands. Factor analysis was used to extract structure information from the set of spectra. Four non-noise factors were extracted; two were assigned to crystalline and amorphous sPS and the other two to background. Score images of the crystalline and amorphous sPS factors were used to visualize distribution of those species. The results were compared to the integrated Raman intensity image for the 773 cm−1 band of crystalline sPS.

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Rutlidge, Helen T., and Brian J. Reedy. "Classification of Heterogeneous Solids Using Infrared Hyperspectral Imaging." Applied Spectroscopy 63, no. 2 (February 2009): 172–79. http://dx.doi.org/10.1366/000370209787391914.

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Shao, Yuanyuan, Yukang Shi, Guantao Xuan, Quankai Li, Fuhui Wang, Chengkun Shi, and Zhichao Hu. "Hyperspectral imaging for non-destructive detection of honey adulteration." Vibrational Spectroscopy 118 (January 2022): 103340. http://dx.doi.org/10.1016/j.vibspec.2022.103340.

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Morales-Sillero, Ana, Juan A. Fernández Pierna, George Sinnaeve, Pierre Dardenne, and Vincent Baeten. "Quantification of protein in wheat using near infrared hyperspectral imaging: Performance comparison with conventional near infrared spectroscopy." Journal of Near Infrared Spectroscopy 26, no. 3 (June 2018): 186–95. http://dx.doi.org/10.1177/0967033518780506.

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Hyperspectral imaging is a powerful technique that combines the advantages of near infrared spectroscopy and imaging technologies. Most hyperspectral imaging studies focus on qualitative analysis, but there is growing interest in using such technique for the quantitative analysis of agro-food products in order to use them as universal tools. The overall objective of this study was to compare the performance of a hyperspectral imaging instrument with a classical near infrared instrument for predicting chemical composition. The determination of the protein content of wheat flour was selected as example. Spectra acquisition was made in individual sealed cells using two classical near infrared instruments (NIR-DS and NIR-Perstop) and a near infrared hyperspectral line-scan camera (NIR-HSI). In the latter, they were also acquired in open cells in order to study the possibility of accelerating the measurement process. Calibration models were developed using partial least squares for the full wavelength range of each individual instrument and for the common range between instruments (1120–2424 nm). The partial least squares models were validated using the “leave-one-out” cross-validation procedure and an independent validation set. The results showed that the NIR-HSI system worked as well as the classical near infrared spectrometers when a common wavelength range was used, with an r2 of 0.99 for all instruments and Root Mean Square Error in Prediction (RMSEP) values of 0.15% for NIR-HSI and NIR-DS and 0.16% for NIR-Perstop. The high residual predictive deviation values obtained (8.08 for NIR-DS, 7.92 for NIR-HSI, and 7.56 for NIR-Perstop) demonstrate the precision of the models built. In addition, the prediction performance with open cells was almost identical to that obtained with sealed cells.

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Wang, Qian, Qingli Li, Mei Zhou, Li Sun, Song Qiu, and Yiting Wang. "Melanoma and Melanocyte Identification from Hyperspectral Pathology Images Using Object-Based Multiscale Analysis." Applied Spectroscopy 72, no. 10 (June 19, 2018): 1538–47. http://dx.doi.org/10.1177/0003702818781352.

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Pathological skin imaging analysis is identified as an efficient technique to diagnose melanoma and provide necessary information for treatment. Automatic detection of melanoma and melanocytes in the epidermis area can be a challenging task as a result of the variability of melanocytes and similarity among cytological components. In order to develop a practical and reliable approach to address the issue, this paper proposed a melanoma and melanocyte detection method based on hyperspectral pathology images. Given the abundant and related spectral and spatial information associated with the hyperspectral skin pathological image, an object-based method was first used to construct the image into the object level; then a multiscale descriptor was performed to extract specific features of melanoma and melanocytes. A quantitative evaluation of 100 scenes of hyperspectral pathology images from 49 patients showed the optimal accuracy, sensitivity, and specificity of 94.29%, 95.57%, and 93.15%, respectively. The results can be interpreted that hyperspectral pathology imaging techniques help to detect the melanoma and melanocytes effectively and provide useful information for further segmentation and classification.

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Shi, Songyue, Xiaoxia Gong, Yan Mu, Kevin Finch, and Gerardo Gamez. "Geometric super-resolution on push-broom hyperspectral imaging for plasma optical emission spectroscopy." Journal of Analytical Atomic Spectrometry 33, no. 10 (2018): 1745–52. http://dx.doi.org/10.1039/c8ja00235e.

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Mukasa, Perez, Collins Wakholi, Akbar Faqeerzada Mohammad, Eunsoo Park, Jayoung Lee, Hyun Kwon Suh, Changyeun Mo, et al. "Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy." Journal of Near Infrared Spectroscopy 28, no. 2 (January 21, 2020): 70–80. http://dx.doi.org/10.1177/0967033519898890.

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The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

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Pinet, P. C. "Spectroscopic Imaging of Solid Planetary Surfaces." International Astronomical Union Colloquium 149 (1995): 294–97. http://dx.doi.org/10.1017/s0252921100023186.

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Seen from Sirius through the eye of the telescope, our inner solar system would easily fit within one CCD-pixel. The purpose of the present paper is: i) to provide with a general overview of the use of imaging or 3D-spectroscopy for the study of the solid planetary surfaces, ii) to demonstrate that the analysis of 3D spectroscopic data on the basis of spectral mixture modelling permits to describe the subpixel spectral variability related to mineralogy of the planetary solid surfaces. In the following, a few cases are discussed concerning the remote sensing investigation in the UV-VIS-nIR domain of the lunar, terrestrial and martian surfaces, documented by means of multispectral or hyperspectral data, produced by telescopic, airborne or orbital imaging spectroscopic techniques.

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Seeley, Megan, and Gregory P. Asner. "Imaging Spectroscopy for Conservation Applications." Remote Sensing 13, no. 2 (January 15, 2021): 292. http://dx.doi.org/10.3390/rs13020292.

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As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

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Liao, Zhiyu, Faris Sinjab, Hany M. Elsheikha, and Ioan Notingher. "Optical sectioning in multifoci Raman hyperspectral imaging." Journal of Raman Spectroscopy 49, no. 10 (July 18, 2018): 1660–67. http://dx.doi.org/10.1002/jrs.5450.

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Xu, Jun-Li, Carlos Esquerre, and Da-Wen Sun. "Methods for performing dimensionality reduction in hyperspectral image classification." Journal of Near Infrared Spectroscopy 26, no. 1 (February 2018): 61–75. http://dx.doi.org/10.1177/0967033518756175.

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This paper provides several useful strategies for performing the dimensionality reduction in hyperspectral imaging data, with detailed command line scripts in the Matlab computing language as the supplementary data. Due to the vast number of data dimensionality reduction methods available, this paper will mainly focus on some commonly used approaches adopted in hyperspectral imaging. In this work, transformation-based methods include principal component analysis and linear discriminant analysis, while band selection methods are comprised of partial least squares regression combined with the variable importance in the projection scores, selectivity ratio, and significance multivariate correlation; Monte Carlo sampling-based methods including enhanced Monte Carlo variable selection and competitive adaptive reweighted sampling; model population analysis-based methods from libPLS including uninformative variable elimination, random frog, and PHADIA; Matlab built-in functions for feature selection including Relieff, stepwise regression, and sequential feature selection; and the selection method guided by genetic algorithm. The example data included in supplementary material, also available for download, will be used to simplify decision tree models for differentiation of white stripe and red muscle pixels on salmon fillets, since classification is one of the main application domains of hyperspectral imaging. In this work, there are many original codes and functions developed, such as fast multiple scattering correction preprocessing, outlier detection, optimal cutoff value determination, spikes, and dead spectra identification and correction for hyperspectral image. More importantly, a further selection function based on variance inflation factor is proposed to diagnose and alleviate collinearity problem because collinearity and multicollinearity are always expected to be severe in the spectral data. In this work, step-by-step procedure is provided for easy adaptation of these strategies to individual case.

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Delaney, John K., Paola Ricciardi, Lisha Glinsman, Michael Palmer, and Julia Burke. "Use of near infrared reflectance imaging spectroscopy to map wool and silk fibres in historic tapestries." Analytical Methods 8, no. 44 (2016): 7886–90. http://dx.doi.org/10.1039/c6ay02066f.

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Calin, Mihaela Antonina, Sorin Viorel Parasca, Marian Romeo Calin, and Emil Petrescu. "An Analysis of Human Dorsal Hand Skin Texture Using Hyperspectral Imaging Technique for Assessing the Skin Aging Process." Applied Spectroscopy 71, no. 3 (November 24, 2016): 391–400. http://dx.doi.org/10.1177/0003702816659667.

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Skin texture has become an important issue in recent research with applications in the cosmetic industry and medicine. In this paper, we analyzed the dependence of skin texture features on wavelength as well as on different parameters (age and gender) of human participants using grey-level co-occurrence matrix and hyperspectral imaging technique for a more accurate quantitative assessment of the aging process. A total of 42 healthy participants (men and women; age range, 20–70 years) was enrolled in this study. A region of interest was selected from the hyperspectral images. The results were analyzed in terms of texture using the gray-level co-occurrence matrix which generated four features (homogeneity, contrast, entropy, and correlation). The results showed that most of these features displayed variations with wavelength (the exception was entropy), with higher variations in women. Only correlation in both sexes and contrast in men proved to vary statistically significant with age, making them the targeted variables in future attempts to characterize aging skin using the complex method of hyperspectral imaging. In conclusion, by using hyperspectral imaging some measure of the degree of damage or the aging process of the hand skin can be obtained, mainly in terms of correlation values. At the present time, reasonable explanations that can link the process of skin aging and the above mentioned features could not be found, but deeper investigations are on the way.

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Journal articles on the topic 'Hyperspectral imaging spectroscopy'

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