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Hyperspectral Sensors, Algorithms and Task Performance
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Hyperspectral Sensors, Algorithms and Task Performance

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 21657

Special Issue Editors

Dr. Jason G Zeibel

E-Mail Website
Guest Editor
US Army C5ISR Center, 10221 Burbeck Road, Fort Belvoir, VA 22060, USA
Interests: hyperspectral imaging; photonics; electrooptics and infrared sensing
Dr. Michal Shimoni

E-Mail Website
Guest Editor
Kuva Space, Otakaari 5, 02150 Espoo, Finland
Interests: hyperspectral imaging; spaceborne observation; neural network; machine learning; analytic products

Special Issue Information

Dear Colleagues,

In the last decade, the availability of hyperspectral sensors of many types has increased significantly. This is true across many imaging wavebands and spans the laboratory, land, sea, air, and space.  This explosion of available spectral data has been met with an equal rise in the quality of image processing techniques, including neural networks, autoencoders, and all manners of machine learning algorithms.  This Special Issue examines the interaction between hyperspectral sensor hardware and algorithmic and processing advances. Specifically, it aims to answer the following questions: What currently limits the state of the art in spectral sensor hardware and processing?  What is the minimum quality that spectral sensors must meet?  Can any manner of sensor artifact be overcome with a sufficiently good algorithm and processor?  Does hyperspectral sensor design drive algorithm development or vice versa?  What is the impact of hyperspectral processing acquisition mode and calibration scheme on algorithm development?

Dr. Jason G Zeibel
Dr. Michal Shimoni
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hyperspectral sensor
  • imaging spectroscopy
  • neural network
  • sensor calibration
  • sensor artifacts
  • spectrometer
  • machine learning
  • target detection and identification

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Published Papers (4 papers)

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Research

12 pages, 4115 KiB  
Article
Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging
by Mona Knoblich, Mohammad Al Ktash, Frank Wackenhut, Tim Englert, Jan Stiedl, Hilmar Wittel, Simon Green, Timo Jacob, Barbara Boldrini, Edwin Ostertag, Karsten Rebner and Marc Brecht
Sensors 2024, 24(14), 4680; https://doi.org/10.3390/s24144680 - 19 Jul 2024
Viewed by 1105
Abstract
In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a [...] Read more.
In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225–400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R2cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes. Full article
(This article belongs to the Special Issue Hyperspectral Sensors, Algorithms and Task Performance)
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12 pages, 1466 KiB  
Article
LeafSpec-Dicot: An Accurate and Portable Hyperspectral Imaging Device for Dicot Leaves
by Xuan Li, Ziling Chen, Jialei Wang and Jian Jin
Sensors 2023, 23(7), 3687; https://doi.org/10.3390/s23073687 - 2 Apr 2023
Cited by 5 | Viewed by 5711
Abstract
Soybean is one of the world’s most consumed crops. As the human population continuously increases, new phenotyping technology is needed to develop new soybean varieties with high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most used technologies for phenotyping. [...] Read more.
Soybean is one of the world’s most consumed crops. As the human population continuously increases, new phenotyping technology is needed to develop new soybean varieties with high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most used technologies for phenotyping. The current HSI techniques with indoor imaging towers and unmanned aerial vehicles (UAVs) suffer from multiple major noise sources, such as changes in ambient lighting conditions, leaf slopes, and environmental conditions. To reduce the noise, a portable single-leaf high-resolution HSI imager named LeafSpec was developed. However, the original design does not work efficiently for the size and shape of dicot leaves, such as soybean leaves. In addition, there is a potential to make the dicot leaf scanning much faster and easier by automating the manual scan effort in the original design. Therefore, a renovated design of a LeafSpec with increased efficiency and imaging quality for dicot leaves is presented in this paper. The new design collects an image of a dicot leaf within 20 s. The data quality of this new device is validated by detecting the effect of nitrogen treatment on soybean plants. The improved spatial resolution allows users to utilize the Normalized Difference Vegetative Index (NDVI) spatial distribution heatmap of the entire leaf to predict the nitrogen content of a soybean plant. This preliminary NDVI distribution analysis result shows a strong correlation (R2 = 0.871) between the image collected by the device and the nitrogen content measured by a commercial laboratory. Therefore, it is concluded that the new LeafSpec-Dicot device can provide high-quality hyperspectral leaf images with high spatial resolution, high spectral resolution, and increased throughput for more accurate phenotyping. This enables phenotyping researchers to develop novel HSI image processing algorithms to utilize both spatial and spectral information to reveal more signals in soybean leaf images. Full article
(This article belongs to the Special Issue Hyperspectral Sensors, Algorithms and Task Performance)
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22 pages, 6163 KiB  
Article
Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability
by Salah Eddine Brezini and Yannick Deville
Sensors 2023, 23(4), 2341; https://doi.org/10.3390/s23042341 - 20 Feb 2023
Cited by 8 | Viewed by 3324
Abstract
The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been [...] Read more.
The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been proposed to tackle this problem, particularly those based on spectral unmixing and using parametric models. Nevertheless, the reported methods need a large number of parameters to address spectral variability, which inevitably yields a higher computation time compared to the standard hypersharpening methods. In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the Automated Extraction of Endmember Bundles (AEEB), and the sparsity-based method called Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL), is introduced. This new method called Hyperspectral Super-resolution with Spectra Bundles dealing with Spectral Variability (HSB-SV) was tested on both synthetic and real data. Experimental results showed that HSB-SV provides sharpened products with higher spectral and spatial reconstruction fidelities with a very low computational complexity compared to other methods dealing with spectral variability, which are the main contributions of the designed method. Full article
(This article belongs to the Special Issue Hyperspectral Sensors, Algorithms and Task Performance)
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16 pages, 49083 KiB  
Article
Active and Low-Cost Hyperspectral Imaging for the Spectral Analysis of a Low-Light Environment
by Yang Tang, Shuang Song, Shengxi Gui, Weilun Chao, Chinmin Cheng and Rongjun Qin
Sensors 2023, 23(3), 1437; https://doi.org/10.3390/s23031437 - 28 Jan 2023
Cited by 36 | Viewed by 10361
Abstract
Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. [...] Read more.
Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. Such systems require an external light source to illuminate the objects, to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting and the imaging system cannot function in a dark or low-light environment. This work develops a prototype system for active hyperspectral imaging, which actively emits diverse single-wavelength light rays at a specific frequency when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is more standardized to extract reflectance information; second, the system is capable of focusing on the desired spectral range by adjusting the number and type of LEDs; third, an active system could be mechanically easier to manufacture, since it does not require complex band filters as used in passive systems. Three lab experiments show that such a design is feasible and could yield informative hyperspectral images in low light or dark environments: (1) spectral analysis: this system’s hyperspectral images improve food ripening and stone type discernibility over RGB images; (2) interpretability: this system’s hyperspectral images improve machine learning accuracy. Therefore, it can potentially benefit the academic and industry segments, such as geochemistry, earth science, subsurface energy, and mining. Full article
(This article belongs to the Special Issue Hyperspectral Sensors, Algorithms and Task Performance)
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