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Exploratory Visual Analysis of Multispectral EO Images Based on DNN

Neagoe, Iulia and Faur, Daniela and Vaduva, Corina and Datcu, Mihai (2018) Exploratory Visual Analysis of Multispectral EO Images Based on DNN. In: 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1-4. IGARSS 2018, 22.-27. Juli 2018, Valencia, Spain. DOI: 10.1109/IGARSS.2018.8518414

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Official URL: https://ieeexplore.ieee.org/document/8518414/authors#authors

Abstract

Exploratory visual analysis is often required to assist human operator to understand and interpret Earth Observation (EO) images. Optimal image representation offers cognitive support in discovering relevant facts about the scene with respect to a particular application. This is of crucial importance for training data sets selection in all Machine Learning tasks, particularly in the design of active learning tools for multispectral (MS) EO data. This paper proposes a deep neural network (DNN) based method to compress, learn and reveal the most significant information included in the spectral bands of EO data in support of relevant visualization for image content analysis. The advanced method uses a DNN to discover the most suggestive pseudo-color representation able to highlight the entire MS image content better than the particular 3 bands selection (R, G, B). We propose the use of information theory and the concept of mutual information to rank the spectral bands based on the amount of information contained, by applying the minimum-redundancy-maximum-relevance (mRMR) criterion on a the image so that we obtain the ranked bands. A DNN stacked autoencoder based paradigm is developed in order to extract and compress in three bands the overall information from the MS EO data. The developed method is demonstrated and validated for Sentinel 2 dataset.

Item URL in elib:https://elib.dlr.de/123434/
Document Type:Conference or Workshop Item (Speech)
Title:Exploratory Visual Analysis of Multispectral EO Images Based on DNN
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Neagoe, IuliaUniversity Politehnica of BucharestUNSPECIFIED
Faur, DanielaUniversity Politehnica BucharestUNSPECIFIED
Vaduva, CorinaPolitehnica University of Bucharest, RomaniaUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:July 2018
Journal or Publication Title:2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/IGARSS.2018.8518414
Page Range:pp. 1-4
Status:Published
Keywords:DNN, minimum-redundancy-maximum-relevance, Sentinel-2
Event Title:IGARSS 2018
Event Location:Valencia, Spain
Event Type:international Conference
Event Dates:22.-27. Juli 2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:28 Nov 2018 14:38
Last Modified:01 Aug 2019 03:00

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