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Hyperspectral/Multispectral Sensing Technologies for Spectral Cameras and Image Sensors
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Hyperspectral/Multispectral Sensing Technologies for Spectral Cameras and Image Sensors

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

Deadline for manuscript submissions: closed (10 April 2025) | Viewed by 6240

Special Issue Editors

Dr. Julio Francisco Rufo Torres

E-Mail Website
Guest Editor
Departamento de Ingeniería Industrial, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
Interests: optical camera communication; visible light communications; spectral signature multiplexing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jose Alberto Rabadan Borges

E-Mail Website
Guest Editor
Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
Interests: optical wireless communications; VLC/OCC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spectral imaging, enabled by spectral cameras and optical sensors, merges the capabilities of digital imaging and spectroscopy by capturing the geometric image across multiple narrow spectral bands, expanding to cover visible, near-infrared, and shortwave infrared spectrums. This approach enables the detection of optical characteristics in objects that are typically unseen by traditional cameras or the human eye. These spectral attributes directly correspond to the chemical composition of an object, facilitating tasks such as object detection, identification, classification, segmentation, and enhanced color characterization. This sensing technology has emerged as a powerful tool across various fields including remote sensing, agriculture, biomedical imaging, and industrial inspection.

This Special Issue attempts to address recent advancements in spectral camera technologies, focusing on both hyperspectral and multispectral imaging systems. It covers key aspects such as spectral camera design, spectral imaging, spatial resolution, optical camera communication, and signal processing, along with their advantages and limitations. Related sensing applications and techniques of spectral cameras in various fields can also be covered in this Special Issue, emphasizing the significance of this technology in environmental monitoring, precision agriculture, medical diagnosis, material characterization, and joint communications and sensing.

Dr. Julio Francisco Rufo Torres
Prof. Dr. Jose Alberto Rabadan Borges
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • spectral cameras
  • hyperspectral/multispectral imaging
  • spectral imaging
  • spatial and spectral resolution
  • optical camera communications
  • imaging modalities
  • optical wireless sensor network
  • spectral signature multiplexing

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Published Papers (3 papers)

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Research

18 pages, 10309 KiB  
Article
Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach
by Huasheng Sun, Lei Guo and Yuan Zhang
Sensors 2025, 25(8), 2604; https://doi.org/10.3390/s25082604 - 20 Apr 2025
Viewed by 127
Abstract
Land surface reflectance is a basic physical parameter in many quantitative remote sensing models. However, the existing reflectance conversion techniques for drone-based (or UAV-based) remote sensing need further improvement and optimization due to either cumbersome operational procedures or inaccurate results. To tackle this [...] Read more.
Land surface reflectance is a basic physical parameter in many quantitative remote sensing models. However, the existing reflectance conversion techniques for drone-based (or UAV-based) remote sensing need further improvement and optimization due to either cumbersome operational procedures or inaccurate results. To tackle this problem, this study proposes a novel method to mathematically implement the separation of direct and scattering radiation using a self-developed multi-angle light intensity device. The verification results from practical experiments demonstrate that the proposed method has strong adaptability, as it can obtain accurate surface reflectance even under complicated conditions where both illumination intensity and component change simultaneously. Among the six selected typical land cover types (i.e., lake water, slab stone, shrub, green grass, red grass, and dry grass), green grass has the highest error among the five multispectral bands with a mean absolute error (MAE) of 1.59%. For all land cover types, the highest MAE of 1.01% is found in the red band. The above validation results indicate that the proposed land surface reflectance conversion method has considerably high accuracy. Therefore, the study results may provide valuable references for quantitative remote sensing applications of drone-based multispectral data, as well as the design of future multispectral drones. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Sensing Technologies for Spectral Cameras and Image Sensors)
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18 pages, 2138 KiB  
Article
Realisation of an Application Specific Multispectral Snapshot-Imaging System Based on Multi-Aperture-Technology and Multispectral Machine Learning Loops
by Lennard Wunsch, Martin Hubold, Rico Nestler and Gunther Notni
Sensors 2024, 24(24), 7984; https://doi.org/10.3390/s24247984 - 14 Dec 2024
Viewed by 4333
Abstract
Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi [...] Read more.
Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi aperture imaging, which enables a simultaneous capture (snapshot) of spectrally selective and spatially resolved scene information. There are some limiting factors for the spectral resolution when implementing this imaging principle, e.g., usable sensor resolutions and area, and required spatial scene resolution or optical complexity. Careful analysis is therefore needed for the specification of the multispectral system properties and its realisation. In this work we present a systematic approach for the application-related implementation of this kind of MSI. We focus on spectral system modeling, data analysis, and machine learning to build a universally usable multispectral loop to find the best sensor configuration. The approach presented is demonstrated and tested on the classification of waste, a typical application for multispectral imaging. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Sensing Technologies for Spectral Cameras and Image Sensors)
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17 pages, 1961 KiB  
Article
Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction
by Xian-Hua Han, Jian Wang and Yen-Wei Chen
Sensors 2024, 24(22), 7362; https://doi.org/10.3390/s24227362 - 18 Nov 2024
Viewed by 1245
Abstract
Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. [...] Read more.
Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. However, these approaches have to break the optimization task into two sub-problems, solving them iteratively over multiple stages, which leads to large models and high computational overheads. This study presents a simple yet effective method that passes the degradation information (sensing mask) through a deep learning network to disentangle the degradation and the latent target’s representations. Specifically, we design a lightweight MLP block to capture non-local similarities and long-range dependencies across both spatial and spectral domains, and investigate an attention-based mask modelling module to achieve the spatial–spectral-adaptive degradation representationthat is fed to the MLP-based network. To enhance the information flow between MLP blocks, we introduce a multi-level fusion module and apply reconstruction heads to different MLP features for deeper supervision. Additionally, we combine the projection loss from compressive measurements with reconstruction loss to create a dual-domain loss, ensuring consistent optical detection during HS reconstruction. Experiments on benchmark HS datasets show that our method outperforms state-of-the-art approaches in terms of both reconstruction accuracy and efficiency, reducing computational and memory costs. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Sensing Technologies for Spectral Cameras and Image Sensors)
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