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Software and Hardware Development for Applications Using Point or Imaging...
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Software and Hardware Development for Applications Using Point or Imaging Spectroscopy Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4208

Special Issue Editors

Dr. Véronique M. Gomes

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences, (CITAB), University of Trás-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal
Interests: machine learning; deep learning; data-driven modeling; hyperspectral imaging; precision agriculture
Dr. Armando Fernandes

E-Mail Website
Guest Editor
INOV — Instituto de Engenharia de Sistemas e Computadores Inovação, 1000-029 Lisboa, Portugal
Interests: machine learning; neural networks; hyperspectral technology; forest fire detection; agriculture applications
Prof. Dr. Pedro Melo-Pinto

E-Mail Website
Guest Editor
1. CITAB, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
2. Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal
Interests: computer vision; machine learning; hyperspectral imaging; image classification; object detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue, we are interested in compiling works regarding developments in multispectral and hyperspectral imaging, with emphasis on, but not limited to, hardware developments and field applications of the technology. Articles that present results regarding point spectroscopy are also welcome.

Regarding the development of hardware, we are particularly interested in new technologies, setups, and layouts that reduce camera costs and allow the widespread use of the technology. Miniaturization is also a topic of interest, together with the possibility of the use of cameras in drones. 

Regarding applications, some possible areas of research are agriculture and fisheries, forest surveillance, water quality, mineral mapping, food quality and safety, automotive quality control, and any subtopic relating to these. Articles regarding applications in airborne or spaceborne sensors are also welcome. In addition, studies concerning the use of these types of cameras in factories will be highly relevant due to an increasing demand for high-quality products/processes at lower costs.

Studies regarding novel machine learning algorithms or state-of-the-art neural network architectures applied to multispectral and hyperspectral imaging solutions will be also valued, with emphasis, for instance, for spectral super-resolution, multispectral to hyperspectral image mapping, or RGB to multispectral imaging mapping, which may help in the integration of less expensive solutions while still using predictive models trained from data with higher spectral resolutions, with minimal losses in performance, during classification or regression tasks.

Dr. Véronique M. Gomes
Dr. Armando Fernandes
Prof. Dr. Pedro Melo-Pinto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Remote Sensing 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 2700 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 imaging
  • multispectral imaging
  • point spectroscopy
  • machine learning for applications
  • low-cost hardware
  • hardware miniaturization
  • industrial Applications

Published Papers (2 papers)

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Research

20 pages, 4653 KiB  
Article
Classification of Fish Species Using Multispectral Data from a Low-Cost Camera and Machine Learning
by Filipe Monteiro, Vasco Bexiga, Paulo Chaves, Joaquim Godinho, David Henriques, Pedro Melo-Pinto, Tiago Nunes, Fernando Piedade, Nelson Pimenta, Luis Sustelo and Armando M. Fernandes
Remote Sens. 2023, 15(16), 3952; https://doi.org/10.3390/rs15163952 - 09 Aug 2023
Cited by 1 | Viewed by 1704
Abstract
This work creates a fish species identification tool combining a low-cost, custom-made multispectral camera called MultiCam and a trained classification algorithm for application in the fishing industry. The objective is to assess, non-destructively and using reflectance spectroscopy, the possibility of classifying the spectra [...] Read more.
This work creates a fish species identification tool combining a low-cost, custom-made multispectral camera called MultiCam and a trained classification algorithm for application in the fishing industry. The objective is to assess, non-destructively and using reflectance spectroscopy, the possibility of classifying the spectra of small fish neighborhoods instead of the whole fish for situations where fish are not completely visible, and use the classification to estimate the percentage of each fish species captured. To the best of the authors’ knowledge, this is the first work to study this possibility. The multispectral imaging device records images from 10 horse mackerel, 10 Atlantic mackerel, and 30 sardines, the three most abundant fish species in Portugal. This results in 48,741 spectra of 5 × 5 pixel regions for analysis. The recording occurs in twelve wavelength bands from 390 nm to 970 nm. The bands correspond to filters with the peculiarity of being highpass to keep the camera cost low. Using a Teflon tape white reference is also relevant to control the overall cost. The tested machine learning algorithms are k-nearest neighbors, multilayer perceptrons, and support vector machines. In general, the results are better than random guessing. The best classification comes from support vector machines, with a balanced accuracy of 63.8%. The use of Teflon does not seem to be detrimental to this result. It seems possible to obtain an equivalent accuracy with ten cameras instead of twelve. Full article
(This article belongs to the Special Issue Software and Hardware Development for Applications Using Point or Imaging Spectroscopy Data)
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23 pages, 2595 KiB  
Article
Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
by Dennis D. Langer, Milica Orlandić, Sivert Bakken, Roger Birkeland, Joseph L. Garrett, Tor A. Johansen and Asgeir J. Sørensen
Remote Sens. 2023, 15(15), 3756; https://doi.org/10.3390/rs15153756 - 28 Jul 2023
Cited by 3 | Viewed by 2057
Abstract
Hyperspectral imaging is a powerful remote sensing technology, but its use in space is limited by the large volume of data it produces, which leads to a downlink bottleneck. Therefore, most payloads to date have been oriented towards demonstrating the scientific usefulness of [...] Read more.
Hyperspectral imaging is a powerful remote sensing technology, but its use in space is limited by the large volume of data it produces, which leads to a downlink bottleneck. Therefore, most payloads to date have been oriented towards demonstrating the scientific usefulness of hyperspectral data sporadically over diverse areas rather than detailed monitoring of spatio-spectral dynamics. The key to overcoming the data bandwidth limitation is to process the data on-board the satellite prior to downlink. In this article, the design, implementation, and in-flight demonstration of the on-board processing pipeline of the HYPSO-1 cube-satellite are presented. The pipeline provides not only flexible image processing but also reliability and resilience, characterized by robust booting and updating procedures. The processing time and compression rate of the simplest pipeline, which includes capturing, binning, and compressing the image, are analyzed in detail. Based on these analyses, the implications of the pipeline performance on HYPSO-1’s mission are discussed. Full article
(This article belongs to the Special Issue Software and Hardware Development for Applications Using Point or Imaging Spectroscopy Data)
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