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Remote Sensing
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Recent Progress in Hyperspectral Remote Sensing Data Processing
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Recent Progress in Hyperspectral Remote Sensing Data Processing

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

Deadline for manuscript submissions: 26 October 2024 | Viewed by 4263

Special Issue Editors

Prof. Dr. Joan Serra-Sagristà

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Guest Editor
Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Campus UAB, 08193 Cerdanyola del Vallès, Spain
Interests: remote sensing data compression; source data coding
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vladimir Lukin

E-Mail Website
Guest Editor
Department of Information-Communication Technologies, National Aerospace University, Chkalova Str., 61070 Kharkov, Ukraine
Interests: image filtering in remote sensing applications; image compression; image filtering, medical imaging; image quality metrics; machine learning in biomedicine
Special Issues, Collections and Topics in MDPI journals
Dr. Benoit Vozel

E-Mail Website
Guest Editor
Institut d’Electronique et des Technologies du numéRique, IETR UMR CNRS 6164, University of Rennes, 22305 Lannion, France
Interests: blind estimation of degradation characteristics (noise, PSF); blind restoration of multicomponent images; multimodal image correction; multicomponent image compression; multi-channel adaptive processing of signals and images; unsupervised machine learning and deep learning; multi-mode remote sensing data processing; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing continues to attract the interest of both industry and academia, as more new applications appear that show its full potential. With the advent of NewSpace and the launch of small satellite constellations, hyperspectral data acquisition is becoming more widespread. This deployment of new hyperspectral sensors entails a wide range of challenges, from the design of more powerful and precise cameras through to the configuration of the systems on board, taking in compression techniques that allow a more efficient transmission, and the multiple techniques of processing on Earth.

This Special Issue is addressed to all researchers and professionals working in the field of hyperspectral data processing, and expects original contributions that describe novelties and innovations in any of the processing stages.

The Special Issue focuses on hyperspectral data, but papers showing progress in multispectral, ultraspectral or other types of remote sensing data are also welcome.

Prof. Dr. Joan Serra-Sagristà
Prof. Dr. Vladimir Lukin
Dr. Benoit Vozel
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 data processing
  • remote sensing data processing
  • classification, segmentation, and detection
  • transmission and compression
  • machine-learning based remote sensing processing

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Published Papers (3 papers)

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Research

19 pages, 10451 KiB  
Article
Lossy Compression of Single-channel Noisy Images by Modern Coders
by Sergii Kryvenko, Vladimir Lukin and Benoit Vozel
Remote Sens. 2024, 16(12), 2093; https://doi.org/10.3390/rs16122093 - 10 Jun 2024
Viewed by 662
Abstract
Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of [...] Read more.
Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of its components are noisy. This fact needs to be taken into account since noise presence leads to specific effects in lossy compressed data. The main effect is the possible existence of the optimal operation point (OOP) shown for JPEG, JPEG2000, some coders based on the discrete cosine transform (DCT), and the better portable graphics (BPG) encoder. However, the performance of such modern coders as AVIF and HEIF with application to noisy images has not been studied yet. In this paper, analysis is carried out for the case of additive white Gaussian noise. We demonstrate that OOP can exist for AVIF and HEIF and the performance characteristics in it are quite similar to those for the BPG encoder. OOP exists with a higher probability for images of simpler structure and/or high-intensity noise, and this takes place according to different metrics including visual quality ones. The problems of providing lossy compression by AVIF or HEIF are shown and an initial solution is proposed. Examples for test and real-life remote-sensing images are presented. Full article
(This article belongs to the Special Issue Recent Progress in Hyperspectral Remote Sensing Data Processing)
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22 pages, 4746 KiB  
Article
SNOWTRAN: A Fast Radiative Transfer Model for Polar Hyperspectral Remote Sensing Applications
by Alexander Kokhanovsky, Maximilian Brell, Karl Segl and Sabine Chabrillat
Remote Sens. 2024, 16(2), 334; https://doi.org/10.3390/rs16020334 - 14 Jan 2024
Cited by 1 | Viewed by 1443
Abstract
In this work, we develop a software suite for studies of atmosphere–underlying SNOW-spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that the aerosol load is extremely low, the [...] Read more.
In this work, we develop a software suite for studies of atmosphere–underlying SNOW-spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that the aerosol load is extremely low, the proposed theory does not require the numerical procedures for the solution of the radiative transfer equation and is based on analytical equations for the spectral nadir reflectance and simple approximations for the local optical properties of atmosphere and snow. The developed model is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. A new, fast technique for the determination of the snow grain size and assessment of the snowpack vertical inhomogeneity is then proposed and further demonstrated on EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The results revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 that was linked to a warming event and a four times larger grain size at Aviator Glacier compared with Dome C. Full article
(This article belongs to the Special Issue Recent Progress in Hyperspectral Remote Sensing Data Processing)
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15 pages, 3534 KiB  
Communication
A Scalable Reduced-Complexity Compression of Hyperspectral Remote Sensing Images Using Deep Learning
by Sebastià Mijares i Verdú, Johannes Ballé, Valero Laparra, Joan Bartrina-Rapesta, Miguel Hernández-Cabronero and Joan Serra-Sagristà
Remote Sens. 2023, 15(18), 4422; https://doi.org/10.3390/rs15184422 - 8 Sep 2023
Cited by 1 | Viewed by 1453
Abstract
Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost [...] Read more.
Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. To address these issues, a channel clusterisation strategy is proposed, which reduces the computational demands of learned compression methods for real scenarios and is scalable for different sources of data with varying numbers of bands. The proposed method is compatible with an embedded implementation for state-of-the-art on board hardware, a first for a ML hyperspectral data compression method. In terms of coding performance, our proposal surpasses established lossy methods such as JPEG 2000 preceded by a spectral Karhunen-Loève Transform (KLT), in clusters of 3 to 7 bands, achieving a PSNR improvement of, on average, 9 dB for AVIRIS and 3 dB for Hyperion images. Full article
(This article belongs to the Special Issue Recent Progress in Hyperspectral Remote Sensing Data Processing)
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