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Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures

Datcu, Mihai und Schwarz, Gottfried und Dumitru, Corneliu Octavian (2020) Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures. In: Recent Trends in Artificial Neural Networks IntechOpen. Seiten 1-12. doi: 10.5772/intechopen.90910.

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Offizielle URL: https://www.intechopen.com/books/recent-trends-in-artificial-neural-networks-from-training-to-prediction/deep-learning-training-and-benchmarks-for-earth-observation-images-data-sets-features-and-procedures

Kurzfassung

Deep learning methods are often used for image classification or local object segmentation. The corresponding test and validation data sets are an integral part of the learning process, and also of the algorithm performance evaluation. High and particularly very high resolution (VHR) Earth observation (EO) applications based on satellite images primarily aim at the semantic labeling of land cover structures or objects as well as of temporal evolution classes. However, one of the main EO objectives is physical parameter retrievals such as temperatures, precipitation, and crop yield predictions. Therefore, we need reliably labeled data sets and tools to train the developed algorithms, and to assess the performance of our Deep Learning paradigms. Generally, imaging sensors generate a visually understandable representation of the observed scene. However, this does not hold for many EO images, where the recorded images only depict a spectral subset of the scattered light field, thus generating an indirect signature of the imaged object. This spots the load of EO image understanding, as a new and particular challenge of Machine Learning (ML) and Artificial Intelligence (AI). The following text reviews and analyses the new approaches of EO imaging leveraging the recent advances in physical process based ML and AI methods, and signal processing. This chapter introduces the basic properties, features, and models for very specific EO cases recorded by VHR multispectral, Synthetic Aperture Radar (SAR), and multi-temporal observations. Further, we describe and discuss procedures and machine learning based tools to generate large semantic training and benchmarking data sets. The particularities of relative data set biases and cross-data set generalization are reviewed, and an algorithmic analysis frame is introduced. Finally, we review and analyze several examples of EO benchmarking data sets.

elib-URL des Eintrags:https://elib.dlr.de/133421/
Dokumentart:Beitrag in einem Lehr- oder Fachbuch
Titel:Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Datcu, MihaiMihai.Datcu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Schwarz, GottfriedGottfried.Schwarz (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:März 2020
Erschienen in:Recent Trends in Artificial Neural Networks
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
DOI:10.5772/intechopen.90910
Seitenbereich:Seiten 1-12
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der HerausgeberHerausgeber-ORCID-iDORCID Put Code
Sadollah, AliUniversity of Science and CultureNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Verlag:IntechOpen
Status:veröffentlicht
Stichwörter:Earth observation, synthetic aperture radar, multispectral, machine learning, and deep learning
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben hochauflösende Fernerkundungsverfahren (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Dumitru, Corneliu Octavian
Hinterlegt am:16 Jan 2020 10:30
Letzte Änderung:14 Mär 2024 15:15

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