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Earth Observation Big Data Intelligence: theory and practice of deep learning and big data mining

Datcu, Mihai and Xu, Feng and Hirose, Akira (2019) Earth Observation Big Data Intelligence: theory and practice of deep learning and big data mining. IGARSS 2019, 2019-07-28 - 2019-08-02, Yokohama, Japan.

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Official URL: https://igarss2019.org/Tutorials.asp#FD4


In the big data era of earth observation, deep learning and other data mining technologies become critical to successful end applications. Over the past several years, there has been exponentially increasing interests related to deep learning techniques applied to remote sensing including not only hyperspectral imagery but also synthetic aperture radar (SAR) imagery. This tutorial has the following three parts. The first part introduces the basic principles of machine learning, and the evolution to deep learning paradigms. It presents the methods of stochastic variational and Bayesian inference, focusing on the methods and algorithms of deep learning generative adversarial networks. Since the data sets are organic part of the learning process, the EO dataset biases pose new challenges. The tutorial answers to open questions on relative data bias, cross-dataset generalization, for very specific EO cases as multispectral, SAR observation with a large variability of imaging parameters and semantic content. The second part introduces the theory of deep neural networks and the practices of deep learning-based remote sensing applications. It introduces the major types of deep neural networks, the backpropagation algorithms, programming toolboxes, and several examples of deep learning-based remote sensing imagery processing. The last part focuses upon data treatment of and applications to phase and polarization in SAR data. Since SAR is a coherent observation, its data properties are quite special and useful for our social activities to provide us with specific feature extraction and discovery. This part deals with deep learning in complex-amplitude and polarization domains as well as so-called data structuration of such multimodal processing.

Item URL in elib:https://elib.dlr.de/131017/
Document Type:Conference or Workshop Item (Lecture)
Title:Earth Observation Big Data Intelligence: theory and practice of deep learning and big data mining
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Hirose, AkiraThe University of TokyoUNSPECIFIEDUNSPECIFIED
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Earth Observation, Big Data Intelligence
Event Title:IGARSS 2019
Event Location:Yokohama, Japan
Event Type:international Conference
Event Start Date:28 July 2019
Event End Date:2 August 2019
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:04 Dec 2019 14:55
Last Modified:24 Apr 2024 20:34

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