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J. Imaging
Special Issues
Advances in Multi/Hyperspectral Imaging
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Advances in Multi/Hyperspectral Imaging

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 5735

Special Issue Editors

Prof. Dr. Alamin Mansouri

E-Mail Website
Guest Editor
Laboratory of Image and Artificial Vision ImViA EA 7535 (Former LE2I), University of Burgundy, 9 Avenue Alain Savary, BP 47870 21078 Dijon CEDEX, France
Interests: color and spectral imaging; appearance capture and modeling; cultural heritage documentation and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Steven Le Moan

E-Mail Website
Guest Editor
Department of Mechanical and Electrical Engineering, Massey University, Palmerston North, New Zealand
Interests: hyperspectral image analysis; unsupervised machine learning; color science

Special Issue Information

Dear Colleagues,

Spectral imaging, the combination of imaging and spectroscopy, has been used for more than five decades in application domains such as agriculture, environmental sciences, astronomy, health and cultural heritage. With the advent of intelligent vision systems and the decrease in hardware cost, multi-/hyper-spectral technologies have become increasingly affordable and practical. In particular, data-driven models have allowed substantial improvements in compressed sensing, super-resolution and pixel labeling, to name only a few. However, these models suffer from the high cost of ground truth data (labels), and, particularly in the case of deep neural networks, from the “black box” effect. In other words, data-driven models can be costly and difficult to interpret.

In an effort to further advance multi/hyperspectral imaging technologies towards more affordability, robustness and practicality, we are calling for original research contributions on topics such as (but not limited to): compressed sensing, representation learning, classification, dimensionality reduction, visualization, color-spectral imaging, explainable AI, applications (remote sensing, food, cultural heritage, bio-medicine, security, etc.). Review papers and use cases are also welcome.

Prof. Dr. Alamin Mansouri
Dr. Steven Le Moan
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • multi-hyperspectral imaging
  • image processing
  • reflectance
  • cultural heritage
  • remote sensing
  • compressed sensing
  • super-resolution
  • representation learning

Published Papers (2 papers)

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Research

21 pages, 3963 KiB  
Article
A Semi-Supervised Reduced-Space Method for Hyperspectral Imaging Segmentation
by Giacomo Aletti, Alessandro Benfenati and Giovanni Naldi
J. Imaging 2021, 7(12), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7120267 - 07 Dec 2021
Cited by 8 | Viewed by 2453
Abstract
The development of the hyperspectral remote sensor technology allows the acquisition of images with a very detailed spectral information for each pixel. Because of this, hyperspectral images (HSI) potentially possess larger capabilities in solving many scientific and practical problems in agriculture, biomedical, ecological, [...] Read more.
The development of the hyperspectral remote sensor technology allows the acquisition of images with a very detailed spectral information for each pixel. Because of this, hyperspectral images (HSI) potentially possess larger capabilities in solving many scientific and practical problems in agriculture, biomedical, ecological, geological, hydrological studies. However, their analysis requires developing specialized and fast algorithms for data processing, due the high dimensionality of the data. In this work, we propose a new semi-supervised method for multilabel segmentation of HSI that combines a suitable linear discriminant analysis, a similarity index to compare different spectra, and a random walk based model with a direct label assignment. The user-marked regions are used for the projection of the original high-dimensional feature space to a lower dimensional space, such that the class separation is maximized. This allows to retain in an automatic way the most informative features, lightening the successive computational burden. The part of the random walk is related to a combinatorial Dirichlet problem involving a weighted graph, where the nodes are the projected pixel of the original HSI, and the positive weights depend on the distances between these nodes. We then assign to each pixel of the original image a probability quantifying the likelihood that the pixel (node) belongs to some subregion. The computation of the spectral distance involves both the coordinates in a features space of a pixel and of its neighbors. The final segmentation process is therefore reduced to a suitable optimization problem coupling the probabilities from the random walker computation, and the similarity with respect the initially labeled pixels. We discuss the properties of the new method with experimental results carried on benchmark images. Full article
(This article belongs to the Special Issue Advances in Multi/Hyperspectral Imaging)
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14 pages, 2376 KiB  
Article
Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms
by Abdellatif Moussaid, Sanaa El Fkihi and Yahya Zennayi
J. Imaging 2021, 7(11), 241; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7110241 - 17 Nov 2021
Cited by 9 | Viewed by 2714
Abstract
Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers [...] Read more.
Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel’s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree’s health and understand the tree’s distribution in the field. Full article
(This article belongs to the Special Issue Advances in Multi/Hyperspectral Imaging)
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