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/ | ||||||||||||
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Document Type: | Article | ||||||||||||
Title: | Cost-Sensitive Multitask Active Learning for Characterization of Urban Environments With Remote Sensing | ||||||||||||
Authors: |
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Date: | June 2018 | ||||||||||||
Journal or Publication Title: | IEEE Geoscience and Remote Sensing Letters | ||||||||||||
Refereed publication: | Yes | ||||||||||||
Open Access: | Yes | ||||||||||||
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 - Earth Observation | ||||||||||||
DLR - Research theme (Project): | R - Security-relevant Earth Observation, R - Remote Sensing and Geo Research | ||||||||||||
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: | 03 Jun 2020 10:55 |
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