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Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN

Hughes, Lloyd and Schmitt, Michael and Mou, Lichao and Wang, Yuanyuan and Zhu, Xiaoxiang (2018) Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN. IEEE Geoscience and Remote Sensing Letters, 15 (5), pp. 784-788. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2018.2799232. ISSN 1545-598X.

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Official URL: https://ieeexplore.ieee.org/document/8314449/


In this letter, we propose a pseudo-siamese convolutional neural network architecture that enables to solve the task of identifying corresponding patches in very high-resolution optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated data set that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently coregistered 3-D point clouds. The satellite images, from which the patches comprising our data set are extracted, show a complex urban scene containing many elevated objects (i.e., buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development toward a generalized multisensor key-point matching procedure.

Item URL in elib:https://elib.dlr.de/114205/
Document Type:Article
Title:Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hughes, Lloydlloyd.hughes (at) tum.deUNSPECIFIED
Schmitt, Michaelm.schmitt (at) tum.deUNSPECIFIED
Mou, LichaoLiChao.Mou (at) dlr.deUNSPECIFIED
Wang, Yuanyuanyuanyuan.wang (at) dlr.deUNSPECIFIED
Zhu, Xiaoxiangxiao.zhu (at) dlr.deUNSPECIFIED
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2018.2799232
Page Range:pp. 784-788
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural networks (CNN), image matching, deep matching
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 > SAR Signal Processing
Deposited By: Mou, LiChao
Deposited On:13 Oct 2017 12:35
Last Modified:01 Jun 2019 03:00

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