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Multispectral Data Analysis for Semantic Assessment-A SNAP Framework for Sentinel-2 Use Case Scenarios

Grivei, Alexandru and Neagoe, Iulia and Georgescu, Florin Andrei and Griparis, Andreea and Vaduva, Corina and Bartalis, Zoltan and Datcu, Mihai (2020) Multispectral Data Analysis for Semantic Assessment-A SNAP Framework for Sentinel-2 Use Case Scenarios. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp. 4429-4442. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2020.3013091. ISSN 1939-1404.

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Official URL: https://ieeexplore.ieee.org/document/9153144

Abstract

Sentinel-2 satellites provide systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of five days. Computer-based data analysis is highly required to extract similarity by processing and to assist human understanding and semantic annotation in support of mapping Earth's surface. This article proposes a data mining concept that uses advanced data visualization and explainable features to enhance relevant aspects in the Sentinel-2 data and enable semantic analysis. There is a two-stage process. At first, spectral, texture, and physical parameters related features are extracted from the data and included in a learning process that models the data content according to statistical similarities. In parallel, the second processing stage maximizes the data impact on the human visual system to help image understanding and interpretation. Target classes are subject to exploratory visual analysis, such that both visual and latent characteristics are revealed to the user. The concept is further implemented as Sentinel-2 dedicated data analysis (DAS-Tool) plugin for the Sentinel Application Platform and deployed as an open-source tool empowering the Earth observation community with fast and reliable results. Accommodating multiple solutions for each processing phase, the plugin enables flexibility in information extraction and knowledge discovery that will bring the best accuracy in mapping applications. For demonstration purposes, the authors focus on a detailed benchmark against reference data (ground truth) for the Southern region of Romania, then use the selected algorithms in a forest fires scenario analysis for the Sydney region in Australia. The processing involves full-size Sentinel-2 images.

Item URL in elib:https://elib.dlr.de/139022/
Document Type:Article
Title:Multispectral Data Analysis for Semantic Assessment-A SNAP Framework for Sentinel-2 Use Case Scenarios
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Grivei, AlexandruUniversity Politehnica of Bucharest, RomaniaUNSPECIFIEDUNSPECIFIED
Neagoe, IuliaUniversity Politehnica of Bucharest, RomaniaUNSPECIFIEDUNSPECIFIED
Georgescu, Florin AndreiUniversity Politehnica of Bucharest, RomaniaUNSPECIFIEDUNSPECIFIED
Griparis, AndreeaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Vaduva, CorinaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bartalis, ZoltanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Datcu, MihaiUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2020
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:13
DOI:10.1109/JSTARS.2020.3013091
Page Range:pp. 4429-4442
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Feature extraction,Data analysis,Semantics,Data Mining,Visualization,Earth,Tools
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Karmakar, Chandrabali
Deposited On:03 Dec 2020 16:00
Last Modified:03 Dec 2020 16:00

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