Special Issue "Multispectral Imaging"

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

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editor

Dr. Pierre Gouton
Website
Guest Editor
Laboratoire ImVia, UFR Sciences et Techniques, Université de Bourgogne, France
Interests: computer vision; robot vision; security acc and monitoring; multispectral imaging; medical image processing, agriculture application

Special Issue Information

Dear Colleagues,

Its now time to conduct a survey on the research in multispectral imaging, both theatrical and applications. Multispectral imaging first started in remote sensing with very wide band sensors. This science is now popular in many areas. In the past two years, we have been able to find in the market snapshot or push-broom cameras dedicated to machine vision. These new cameras are based on new operating systems. They are dedicated to real time, allowing the orderly acquisition of moving objects. Compared to color imaging, multispectral imagery covers several areas, including:

Security
Medicine
Agriculture
Automotive

Research has led to many advances in the last 10 years, but there are still unresolved issues.

Multispectral imaging could be divided into three major areas of research:

  1. The analysis methods of spectral data for their exploitation;
  2. The MSFA (multispectral Filter Array);
  3. Filtering technologies.

There is not a unique approach to solve the problems. This edition expects to propose new methods and new systems of filtering and analyzing multispectral data in both theory and applications.

Multispectral image analysis: Must cover the fields of compression, visualization of the spectral data, identification, deep learning, etc.

The MSFA: Must allow highlighting spectral reconstruction methods and linking with the CFA widely developed for color imaging technologies. It will also include the possibility of using MSFA in real time, for example, snapshot cameras.

Filtering: Must include the methods of selections of spectral bands and new developments of filters based on microelectronics, such as nanotechnology. Contributions will focus on making multispectral cameras more efficient and extending their application to mobile objects.

Prof. Pierre Gouton
Guest Editor

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. 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 1000 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

  • Multispectral technology
  • Multispectral and 3D
  • Spectral and demosaicing
  • MSFA design
  • Multispectal data visualization
  • Spectral bands selection
  • Deep learning
  • System calibration

Published Papers (3 papers)

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Research

Open AccessArticle
Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE
J. Imaging 2020, 6(5), 29; https://doi.org/10.3390/jimaging6050029 - 05 May 2020
Abstract
For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document’s paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging [...] Read more.
For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document’s paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations. Full article
(This article belongs to the Special Issue Multispectral Imaging)
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Open AccessArticle
Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks
J. Imaging 2020, 6(4), 24; https://doi.org/10.3390/jimaging6040024 - 18 Apr 2020
Abstract
Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but it unavoidably introduces unwanted artifacts. In this work, we consider the encoding of [...] Read more.
Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but it unavoidably introduces unwanted artifacts. In this work, we consider the encoding of multispectral observations into high-order tensor structures which can naturally capture multi-dimensional dependencies and correlations, and we propose a resource-efficient compression scheme based on quantized low-rank tensor completion. The proposed method is also applicable to the case of missing observations due to environmental conditions, such as cloud cover. To quantify the performance of compression, we consider both typical image quality metrics as well as the impact on state-of-the-art deep learning-based land-cover classification schemes. Experimental analysis on observations from the ESA Sentinel-2 satellite reveals that even minimal compression can have negative effects on classification performance which can be efficiently addressed by our proposed recovery scheme. Full article
(This article belongs to the Special Issue Multispectral Imaging)
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Open AccessArticle
Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions
J. Imaging 2019, 5(11), 85; https://doi.org/10.3390/jimaging5110085 - 15 Nov 2019
Abstract
This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS [...] Read more.
This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package. Full article
(This article belongs to the Special Issue Multispectral Imaging)
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