Sica, Francescopaolo and Pulella, Andrea and Lopez, Carlos Villamil and Anglberger, Harald and Hänsch, Ronny (2022) Generalization in Object Recognition from SAR Imagery. In: 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, pp. 1007-1010. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2022-07-17 - 2022-07-22, Kuala Lumpur, Malaysia. doi: 10.1109/IGARSS46834.2022.9884427. ISBN 978-166542792-0.
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Official URL: https://ieeexplore.ieee.org/abstract/document/9884427
Abstract
Object recognition in synthetic aperture radar images is a well studied topic that has gained a significant amount of attention within the last decades. Modern approaches are based on machine learning, i.e. deep learning, and often show excellent performance. What is so far missing in the literature is a study dedicated to the generalization capabilities of object recognition approaches, i.e. how well a given system can be transferred to new and previously unseen data. In this paper, the proposed recognition model is trained and tested on a unique dataset of 25 high-resolution TerraSAR-X images (X-band), acquired over four different airports in Staring Spotlight mode. We show how classification performance changes for different application scenarios which require different training and evaluation setups.
Item URL in elib: | https://elib.dlr.de/191313/ | ||||||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||||||||
Title: | Generalization in Object Recognition from SAR Imagery | ||||||||||||||||||||||||
Authors: |
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Date: | July 2022 | ||||||||||||||||||||||||
Journal or Publication Title: | 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 | ||||||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||||||
Open Access: | No | ||||||||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||||||||
In ISI Web of Science: | Yes | ||||||||||||||||||||||||
DOI: | 10.1109/IGARSS46834.2022.9884427 | ||||||||||||||||||||||||
Page Range: | pp. 1007-1010 | ||||||||||||||||||||||||
ISBN: | 978-166542792-0 | ||||||||||||||||||||||||
Status: | Published | ||||||||||||||||||||||||
Keywords: | Deep Learning, Object Detection | ||||||||||||||||||||||||
Event Title: | IEEE International Geoscience and Remote Sensing Symposium (IGARSS) | ||||||||||||||||||||||||
Event Location: | Kuala Lumpur, Malaysia | ||||||||||||||||||||||||
Event Type: | international Conference | ||||||||||||||||||||||||
Event Start Date: | 17 July 2022 | ||||||||||||||||||||||||
Event End Date: | 22 July 2022 | ||||||||||||||||||||||||
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 - Artificial Intelligence | ||||||||||||||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institutes and Institutions: | Microwaves and Radar Institute > SAR Technology Microwaves and Radar Institute > Reconnaissance and Security | ||||||||||||||||||||||||
Deposited By: | Hänsch, Ronny | ||||||||||||||||||||||||
Deposited On: | 01 Dec 2022 13:18 | ||||||||||||||||||||||||
Last Modified: | 24 Apr 2024 20:52 |
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