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AuthorAhishali M.
AuthorKiranyaz, Mustafa Serkan
AuthorInce T.
AuthorGabbouj M.
Available date2022-04-26T12:31:20Z
Publication Date2020
Publication Name2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Proceedings
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/M2GARSS47143.2020.9105312
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086740246&doi=10.1109%2fM2GARSS47143.2020.9105312&partnerID=40&md5=8c9a8c04172c6da20be35bde848adc83
URIhttp://hdl.handle.net/10576/30608
AbstractIn this work, we propose a novel classification approach based on dual-band one-dimensional Convolutional Neural Networks (1D-CNNs) for classification of multifrequency polarimetric SAR (PolSAR) data. The proposed approach can jointly learn from C- and L-band data and improve the single band classification accuracy. To the best of our knowledge, this is the first study that introduces 1D-CNNs to land use/land cover classification domain using PolSAR data. The proposed approach aims to achieve maximum classification accuracy by one-time training over multiple frequency bands with limited labelled data. Moreover, the proposed dual-band 1D-CNN approach yields a superior computational efficiency compared to the deep 2D-CNN based approaches. The performed experiments using AIRSAR PolSAR image over San Diego region at C- and L-bands have shown that the proposed approach is able to simultaneously learn from the C- and L-band SAR data and achieves an elegant classification performance with minimal complexity.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectComputational efficiency
Convolution
Convolutional neural networks
Geology
Image classification
Land use
One dimensional
Radar imaging
Remote sensing
Classification accuracy
Classification approach
Classification performance
Land use/land cover
Multi frequency
Multiple frequency
Polarimetric SAR
Single band
Classification (of information)
TitleMultifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks
TypeConference Paper
Pagination73-76
dc.accessType Abstract Only


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