scholarly journals Mid-Infrared Compressive Hyperspectral Imaging

2021 ◽  
Vol 13 (4) ◽  
pp. 741
Author(s):  
Shuowen Yang ◽  
Xiang Yan ◽  
Hanlin Qin ◽  
Qingjie Zeng ◽  
Yi Liang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Side Information ◽  
Computational Imaging ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Acquisition Time ◽  
Reconstruction Method ◽  
Mid Infrared ◽  
Spectral Channels

Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems.

scholarly journals Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Zhang ◽  
Biyao Wang ◽  
Pengtao Yan ◽  
Kunlun Wang ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Injection ◽  
Principal Component ◽  
Local Extremum ◽  
Hyperspectral Data ◽  
Identification Accuracy ◽  
Hyperspectral Images ◽  
Nondestructive Identification ◽  
Som Network ◽  
Weighting Coefficients

For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

scholarly journals Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

2020 ◽  
Author(s):  
Mohammad Al Ktash ◽  
Otto Hauler ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Near Infrared ◽  
Dominant Role ◽  
Principal Component ◽  
Industrial Applications ◽  
Acquisition Time ◽  
Hydroxyl Groups ◽  
Short Acquisition Time ◽  
Different Types

Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

CHEMOMETRIC STRATEGIES FOR NEAR INFRARED HYPERSPECTRAL IMAGING ANALYSIS: CLASSIFICATION OF SEED GENOTYPES

2021 ◽  
Author(s):  
Simone Simões ◽  
Priscilla Rocha ◽  
Everaldo Paulo Medeiros ◽  
Carolina Silva
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Hyperspectral Images ◽  
Imaging Analysis

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based in HSI in the...

scholarly journals Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water

2020 ◽  
Vol 12 (13) ◽  
pp. 2070
Author(s):  
Geonwoo Kim ◽  
Insuck Baek ◽  
Matthew D. Stocker ◽  
Jaclyn E. Smith ◽  
Andrew L. Van Tassell ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Water Sampling ◽  
Floating Platform ◽  
Irrigation Pond

This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source.

scholarly journals Molecule Sensitive Optical Imaging and Monitoring Techniques—A Review of Applications in Micro-Process Engineering

Micromachines ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 353 ◽  
Author(s):  
Marcel Nachtmann ◽  
Julian Deuerling ◽  
Matthias Rädle
Keyword(s):  
Near Infrared ◽  
Scattered Light ◽  
Coupled Systems ◽  
Optical Methods ◽  
Acquisition Time ◽  
Fluid Composition ◽  
Actual Measurement ◽  
Mid Infrared ◽  
Advantages And Disadvantages ◽  
Small Probe

This paper provides an overview of how molecule-sensitive, spatially-resolved technologies can be applied for monitoring and measuring in microchannels. The principles of elastic light scattering, fluorescence, near-infrared, mid-infrared, and Raman imaging, as well as combination techniques, are briefly presented, and their advantages and disadvantages are explained. With optical methods, images can be acquired both scanning and simultaneously as a complete image. Scanning technologies require more acquisition time, and fast moving processes are not easily observable. On the other hand, molecular selectivity is very high, especially in Raman and mid-infrared (MIR) scanning. For near-infrared (NIR) images, the entire measuring range can be simultaneously recorded with indium gallium arsenide (InGaAs) cameras. However, in this wavelength range, water is the dominant molecule, so it is sometimes necessary to use complex learning algorithms that increase the preparation effort before the actual measurement. These technologies excite molecular vibrations in a variety of ways, making these methods suitable for specific products. Besides measurements of the fluid composition, technologies for particle detection are of additional importance. With scattered light techniques and evaluation according to the Mie theory, particles in the range of 0.2–1 µm can be detected, and fast growth processes can be observed. Local multispectral measurements can also be carried out with fiber optic-coupled systems through small probe heads of approximately 1 mm diameter.

Near-infrared hyperspectral image at a glance: Some personal thoughts

NIR news ◽  
2020 ◽  
Vol 31 (5-6) ◽  
pp. 8-14
Author(s):  
José Manuel Amigo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Constructive Criticism ◽  
Constructive Discussion

First of all, I want to transmit my most humble thanks to all people who believe that I deserve the “2019 Thomas Hirschfeld” award (kindly supported by FOSS) for my work on near-infrared spectroscopy and, especially, applied on hyperspectral images. I must confess that this award caught me by surprise and that I felt a bit overwhelmed when I received it. It is an honour full of respect and responsibility. I have been given the opportunity of writing this article, and I will profit it to express different personal thoughts about general but relevant aspects of near infrared applied to hyperspectral imaging. Also, since I am more a practitioner in chemometrics (or machine learning or data mining, or …) than a developer, I will also include some insights about the beautiful combination of near-infrared hyperspectral image with chemometrics. This article is just a glimpse of constructive criticism with personal thoughts that comes from my little experience in this field. Therefore, and of course, all opinions here are open for constructive discussion with the only purpose of learning (like the machines do nowadays).

Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging

2020 ◽  
Vol 75 (1) ◽  
pp. 46-56
Author(s):  
Xiaoyun Chen ◽  
Kshitish A. Patankar ◽  
Matthew Larive
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Mixing Time ◽  
Building Blocks ◽  
Acquisition Time ◽  
Blowing Agents ◽  
Spectral Changes ◽  
Methylene Diphenyl Diisocyanate ◽  
Polyurethane Foaming ◽  
Pu Foams

Polyurethane (PU) foams are finding increasingly wider applications ranging from memory foams and mattresses to cushions and insulation materials. They are prepared by reactions between multifunctional isocyanates and polyols as the two main building blocks, along with other additives, including the blowing agents. A non-contact near-infrared (NIR) hyperspectral imaging (HSI) camera was used in this study to monitor PU foaming reactions between a polymeric methylene diphenyl diisocyanate, polyol, and water. Five foams were prepared with three process variables: water content, mixing time, and catalyst levels. Spectral changes characteristic of the PU reactions were observed and clear difference in kinetics could be effectively extracted from such NIR HSI results. The NIR HSI technology offers two substantial advantages over the conventional Fourier transform- (FT-) NIR systems: (i) faster spectral acquisition time and (ii) higher spatial resolution of line images rather than the point measurement. Examples are provided to illustrate these two advantages. The potential to acquire chemical images of PU foams is also demonstrated.

scholarly journals INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

2016 ◽  
Vol III-7 ◽  
pp. 257-261 ◽  
Author(s):  
Helge Aasen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Field Of View ◽  
Hyperspectral Images ◽  
Single Image ◽  
Area Of Interest ◽  
Vegetation Parameter ◽  
Parameter Retrieval

Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. <br><br> This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. <br><br> The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

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