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Spectral Data Meets Machine Learning: From Datasets to Algorithms and...
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Spectral Data Meets Machine Learning: From Datasets to Algorithms and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 17044

Special Issue Editors

Dr. Sina Keller

E-Mail Website
Guest Editor
Karlsruhe Institute of Technology, Englerstrasse 7, 76131 Karlsruhe, Germany
Interests: machine learning; remote sensing; geoinformatics; vulnerability assessment; natural hazards
Dr. Naoto Yokoya

grade E-Mail Website
Guest Editor
RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
Interests: remote sensing; image processing; data fusion; machine learning; disaster management; environmental monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jocelyn Chanussot

E-Mail Website
Guest Editor
Grenoble Institute of Technology, GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, CEDEX, F-38402 Saint Martin d'Hères, France
Interests: image processing; machine learning; mathematical morphology; hyperspectral imaging; data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Martin Weinmann

E-Mail Website
Guest Editor
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstra_e 7, 76131 Karlsruhe, Germany
Interests: computer vision; pattern recognition; machine learning; photogrammetry; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to technological developments and the steadily increasing performance of the information infrastructure, spectral remote sensing systems have rapidly advanced over the last decade. Meanwhile, such systems provide data with high spatial and temporal resolutions. Simultaneously, the performance of the general information infrastructure has improved, mainly affecting the processing, storage, and transmission of data. This performance facilitates the handling of massive amounts of data resulting from the consideration of larger areas, an increasing level of detail, and the use of multi-sensor systems.

Among the methodological advancements, much progress is related to machine learning approaches for either classification or regression tasks. Access to the necessary computational resources and the increasing availability of large volumes of labeled remote sensing data enable researchers to train deeper models. Recent research addresses, for example, the development of new network architectures, opportunities for data fusion, the consideration of only small datasets, the explainability of deep learning approaches, or multi-temporal analyses.

This Special Issue welcomes papers that present innovative methodological advancements, latest results and findings of application-oriented work concerning the analysis of multispectral or hyperspectral data. Besides original work, this Special Issue is intended also to include selected papers representing an extension of work presented at the 2nd HyperMLPA workshop (http://www.spectroexpo.com/hypermlpa/).

Topics include but are not limited to:

  • Spectral data processing
  • New benchmark datasets
  • Feature extraction, feature selection, dimensionality reduction, and data fusion
  • Supervised and unsupervised machine learning
  • Classification/segmentation
  • Regression
  • Change detection
  • Deep learning approaches
  • Multi-temporal analysis
  • Applications for environmental monitoring and industrial processes

Dr. Sina Keller
Dr. Naoto Yokoya
Prof. Dr. Jocelyn Chanussot
Dr. Martin Weinmann
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

  • Spectral data processing
  • Benchmark datasets
  • Feature extraction
  • Data fusion
  • Supervised and unsupervised machine learning
  • Classification / segmentation
  • Regression
  • Change detection
  • Deep learning approaches
  • Multi-temporal analysis

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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27 pages, 10431 KiB  
Article
Glacier Monitoring Based on Multi-Spectral and Multi-Temporal Satellite Data: A Case Study for Classification with Respect to Different Snow and Ice Types
by Janine Florath, Sina Keller, Rodrigo Abarca-del-Rio, Stefan Hinz, Guido Staub and Martin Weinmann
Remote Sens. 2022, 14(4), 845; https://doi.org/10.3390/rs14040845 - 11 Feb 2022
Cited by 7 | Viewed by 4769
Abstract
Remote sensing techniques are frequently applied for the surveying of remote areas, where the use of conventional surveying techniques remains difficult and impracticable. In this paper, we focus on one of the remote glacier areas, namely the Tyndall Glacier area in the Southern [...] Read more.
Remote sensing techniques are frequently applied for the surveying of remote areas, where the use of conventional surveying techniques remains difficult and impracticable. In this paper, we focus on one of the remote glacier areas, namely the Tyndall Glacier area in the Southern Patagonian Icefield in Chile. Based on optical remote sensing data in the form of multi-spectral Sentinel-2 imagery, we analyze the extent of different snow and ice classes on the surface of the glacier by means of pixel-wise classification. Our study comprises three main steps: (1) Labeled Sentinel-2 compliant data are obtained from theoretical spectral reflectance curves, as there are no training data available for the investigated area; (2) Four different classification approaches are used and compared in their ability to identify the defined five snow and ice types, thereof two unsupervised approaches (k-means clustering and rule-based classification via snow and ice indices) and two supervised approaches (Linear Discriminant Analysis and Random Forest classifier); (3) We first focus on the pixel-wise classification of Sentinel-2 imagery, and we then use the best-performing approach for a multi-temporal analysis of the Tyndall Glacier area. While the achieved classification results reveal that all of the used classification approaches are suitable for detecting different snow and ice classes on the glacier surface, the multi-temporal analysis clearly reveals the seasonal development of the glacier. The change of snow and ice types on the glacier surface is evident, especially between the end of ablation season (April) and the end of accumulation season (September) in Southern Chile. Full article
(This article belongs to the Special Issue Spectral Data Meets Machine Learning: From Datasets to Algorithms and Applications)
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22 pages, 22566 KiB  
Article
Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification
by Xin He and Yushi Chen
Remote Sens. 2021, 13(17), 3547; https://doi.org/10.3390/rs13173547 - 6 Sep 2021
Cited by 22 | Viewed by 3477
Abstract
Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become the de-facto standard for HSI classification. It seems that the traditional neural networks such as multi-layer perceptron (MLP) are [...] Read more.
Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become the de-facto standard for HSI classification. It seems that the traditional neural networks such as multi-layer perceptron (MLP) are not competitive for HSI classification. However, in this study, we try to prove that the MLP can achieve good classification performance of HSI if it is properly designed and improved. The proposed Modified-MLP for HSI classification contains two special parts: spectral–spatial feature mapping and spectral–spatial information mixing. Specifically, for spectral–spatial feature mapping, each input sample of HSI is divided into a sequence of 3D patches with fixed length and then a linear layer is used to map the 3D patches to spectral–spatial features. For spectral–spatial information mixing, all the spectral–spatial features within a single sample are feed into the solely MLP architecture to model the spectral–spatial information across patches for following HSI classification. Furthermore, to obtain the abundant spectral–spatial information with different scales, Multiscale-MLP is proposed to aggregate neighboring patches with multiscale shapes for acquiring abundant spectral–spatial information. In addition, the Soft-MLP is proposed to further enhance the classification performance by applying soft split operation, which flexibly capture the global relations of patches at different positions in the input HSI sample. Finally, label smoothing is introduced to mitigate the overfitting problem in the Soft-MLP (Soft-MLP-L), which greatly improves the classification performance of MLP-based method. The proposed Modified-MLP, Multiscale-MLP, Soft-MLP, and Soft-MLP-L are tested on the three widely used hyperspectral datasets. The proposed Soft-MLP-L leads to the highest OA, which outperforms CNN by 5.76%, 2.55%, and 2.5% on the Salinas, Pavia, and Indian Pines datasets, respectively. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the MLP-based methods are still competitive for HSI classification. Full article
(This article belongs to the Special Issue Spectral Data Meets Machine Learning: From Datasets to Algorithms and Applications)
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28 pages, 8349 KiB  
Article
DLR HySU—A Benchmark Dataset for Spectral Unmixing
by Daniele Cerra, Miguel Pato, Kevin Alonso, Claas Köhler, Mathias Schneider, Raquel de los Reyes, Emiliano Carmona, Rudolf Richter, Franz Kurz, Peter Reinartz and Rupert Müller
Remote Sens. 2021, 13(13), 2559; https://doi.org/10.3390/rs13132559 - 30 Jun 2021
Cited by 10 | Viewed by 4143
Abstract
Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing [...] Read more.
Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications. Full article
(This article belongs to the Special Issue Spectral Data Meets Machine Learning: From Datasets to Algorithms and Applications)
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Other

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15 pages, 14466 KiB  
Technical Note
Gaussian Process and Deep Learning Atmospheric Correction
by Bill Basener and Abigail Basener
Remote Sens. 2023, 15(3), 649; https://doi.org/10.3390/rs15030649 - 21 Jan 2023
Cited by 4 | Viewed by 2342
Abstract
Atmospheric correction is the processes of converting radiance values measured at a spectral sensor to the reflectance values of the materials in a multispectral or hyperspectral image. This is an important step for detecting or identifying the materials present in the pixel spectra. [...] Read more.
Atmospheric correction is the processes of converting radiance values measured at a spectral sensor to the reflectance values of the materials in a multispectral or hyperspectral image. This is an important step for detecting or identifying the materials present in the pixel spectra. We present two machine learning models for atmospheric correction trained and tested on 100,000 batches of 40 reflectance spectra converted to radiance using MODTRAN, so the machine learning model learns the radiative transfer physics from MODTRAN. We created a theoretically interpretable Bayesian Gaussian process model and a deep learning autoencoder treating the atmosphere as noise. We compare both methods for estimating gain in the correction model to process for estimating gain within the well-know QUAC method which assumes a constant mean endmember reflectance. Prediction of reflectance using the Gaussian process model outperforms the other methods in terms of both accuracy and reliability. Full article
(This article belongs to the Special Issue Spectral Data Meets Machine Learning: From Datasets to Algorithms and Applications)
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