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Multispectral and Hyperspectral Imaging for Next-Generation Sensing Applications

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

Deadline for manuscript submissions: 5 October 2025 | Viewed by 581

Special Issue Editors


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Guest Editor
Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: hyperspectral image processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: infrared imaging; artificial intelligence; image processing; embedded system
Special Issues, Collections and Topics in MDPI journals
Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: imaging processing; infrared and hyperspectral imaging; small target detection
Special Issues, Collections and Topics in MDPI journals
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China
Interests: infrared and spectral signal processing technologies and theories
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral imaging has emerged as a crucial tool in remote sensing geology, providing essential support for applications such as land and mineral resource management, geological disaster early warning systems, and military functions, including air defenses and camouflage detection. The European Union's white paper, "100 radical innovation breakthroughs for the future," identifies hyperspectral imaging as a disruptive technology poised to significantly impact the global economy. Despite its promise, achieving high spatial, temporal, and spectral resolution simultaneously—termed the "three high resolutions"—remains a significant bottleneck in both military and civilian applications due to inherent trade-offs among these dimensions. This Special Issue focuses on the use of innovative systems for high-resolution hyperspectral imaging, alongside advanced methodologies such as super-resolution techniques, spectral unmixing, anomaly detection, and refined classification processes. Furthermore, given the widespread use of deep learning and the scarcity of training data in remote sensing, this Special Issue will explore advancements in hyperspectral data generation. This research seeks to mitigate the challenge of "data scarcity," facilitating a shift from basic classification to more sophisticated identification of hyperspectral targets. By supporting models with increased parameter scale and complexity, we will provide the theoretical and methodological foundations necessary to advance applications into the era of large-scale remote sensing models.

Dr. Xiaoguang Mei
Dr. Jun Huang
Dr. Fan Fan
Dr. Hao Li
Guest Editors

Manuscript Submission Information

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Keywords

  • hyperspectral imaging
  • remote sensing geology
  • hyperspectral applications
  • three high-resolution imaging
  • data generation

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Published Papers (1 paper)

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Research

11 pages, 3390 KiB  
Article
Material Sensing with Spatial and Spectral Resolution Based on an Integrated Near-Infrared Spectral Sensor and a CMOS Camera
by Ben Delaney, Sjors Buntinx, Don M. J. van Elst, Anne van Klinken, René P. J. van Veldhoven and Andrea Fiore
Sensors 2025, 25(11), 3295; https://doi.org/10.3390/s25113295 - 23 May 2025
Viewed by 259
Abstract
Measuring the composition of materials at a distance is a key requirement in industrial process monitoring, recycling, precision agriculture, and environmental monitoring. Spectral imaging in the visible or near-infrared (NIR) spectral bands provides a potential solution by combining spatial and spectral information, and [...] Read more.
Measuring the composition of materials at a distance is a key requirement in industrial process monitoring, recycling, precision agriculture, and environmental monitoring. Spectral imaging in the visible or near-infrared (NIR) spectral bands provides a potential solution by combining spatial and spectral information, and its application has seen significant growth over recent decades. Low-cost solutions for visible multispectral imaging (MSI) have been developed due to the widespread availability of silicon detectors, which are sensitive in this spectral region. In contrast, development in the NIR has been slower, primarily due to the high cost of indium gallium arsenide (InGaAs) detector arrays required for imaging. This work aims to bridge this gap by introducing a standoff material sensing concept which combines spatial and spectral resolution without the hardware requirements of traditional spectral imaging systems. It combines spatial imaging in the visible range with a CMOS camera and NIR spectral measurement at selected points of the scene using an NIR spectral sensor. This allows the chemical characterization of different objects of interest in a scene without acquiring a full spectral image. We showcase its application in plastic classification, a key functionality in sorting and recycling systems. The system demonstrated the capability to classify visually identical plastics of different types in a standoff measurement configuration and to produce spectral measurements at up to 100 points in a scene. Full article
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