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Machine Learning Techniques for Knowledge Extraction from Satellite Images: Application to Specific Area Types

Dumitru, Corneliu Octavian and Schwarz, Gottfried and Datcu, Mihai (2021) Machine Learning Techniques for Knowledge Extraction from Satellite Images: Application to Specific Area Types. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII, pp. 455-462. ISPRS 2021, 5.-9. July 2021, Nice, France. doi: 10.5194/isprs-archives-XLIII-B3-2021-455-2021. ISSN 1682-1750.

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Official URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/455/2021/isprs-archives-XLIII-B3-2021-455-2021.pdf

Abstract

When we want to extract knowledge form satellite images, several well-known image classification and analysis techniques can be concatenated or combined to gain a more detailed target understanding. In our case, we concentrated on specific extended target areas such as polar ice-covered surfaces, forests shrouded by fire plumes, flooded areas, and shorelines. These image types can be described by characteristic features and statistical relationships. Here, we demonstrate that both multispectral (optical) as well as SAR (Synthetic Aperture Radar) images can be used for knowledge extraction. The free availability of image data provided by the European Sentinel-1 and Sentinel-2 satellites allowed us to conduct a series of experiments that verified our classification approaches. This could already be verified in our recent work by quantitative quality tests.

Item URL in elib:https://elib.dlr.de/142807/
Document Type:Conference or Workshop Item (Poster)
Title:Machine Learning Techniques for Knowledge Extraction from Satellite Images: Application to Specific Area Types
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deUNSPECIFIED
Schwarz, GottfriedGottfried.Schwarz (at) dlr.deUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:July 2021
Journal or Publication Title:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:XLIII
DOI :10.5194/isprs-archives-XLIII-B3-2021-455-2021
Page Range:pp. 455-462
ISSN:1682-1750
Status:Published
Keywords:Machine learning, target areas, flooding, fires, coastal and polar areas, Sentinel-1, Sentinel-2
Event Title:ISPRS 2021
Event Location:Nice, France
Event Type:international Conference
Event Dates:5.-9. July 2021
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 - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:24 Jun 2021 12:44
Last Modified:25 Aug 2021 09:45

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