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Cost-Sensitive Multitask Active Learning for Characterization of Urban Environments With Remote Sensing

Geiß, Christian and Thoma, Matthias and Taubenböck, Hannes (2018) Cost-Sensitive Multitask Active Learning for Characterization of Urban Environments With Remote Sensing. IEEE Geoscience and Remote Sensing Letters, 15 (6), pp. 922-926. IEEE - Institute of Electrical and Electronics Engineers. ISSN 1545-598X

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Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8341760

Abstract

In this letter, we propose a novel cost-sensitive multi-task active learning (CSMTAL) approach. Cost-sensitive active learning (CSAL) methods were recently introduced to specifically minimize labeling efforts emerging from ground surveys. Here, we build upon a CSAL method but compile a set of unlabeled samples from a learning set which can be considered relevant with respect to multiple target variables. To this purpose, a multi-task meta-protocol based on alternating selection is implemented. It comprises a so-called one-sided selection (i.e., single-task AL selection for a reference target variable with simultaneous labeling of the residual target variables) with a changing leading variable in an iterative selection process. Experimental results are obtained for the city of Cologne, Germany. The target variables to be predicted, using features from remote sensing and a Support Vector Machines framework, comprise “building type” and “roof type”. Comparative model accuracy evaluations underline the capability of the CSMTAL method to provide beneficial solutions with respect to a random sampling strategy and non-cost-sensitive multi-task active sampling.

Item URL in elib:https://elib.dlr.de/120168/
Document Type:Article
Title:Cost-Sensitive Multitask Active Learning for Characterization of Urban Environments With Remote Sensing
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Geiß, Christianchristian.geiss (at) dlr.dehttps://orcid.org/0000-0002-7961-8553
Thoma, Matthiasmatthias.thoma (at) hswt.deUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Date:June 2018
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:15
Page Range:pp. 922-926
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Building type, cost-sensitive multitask active learning (CSMTAL), LiDAR, remote sensing, roof type, support vector machines (SVMs), very high-resolution imagery
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 Sicherheitsrelevante Erdbeobachtung, R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Geiß, Christian
Deposited On:13 Jun 2018 09:40
Last Modified:13 Jun 2018 09:40

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