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Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks

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Details

Original languageEnglish
Title of host publication2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Proceedings
PublisherIEEE
Pages73-76
Number of pages4
ISBN (Electronic)9781728121901
DOIs
Publication statusPublished - 1 Mar 2020
Publication typeA4 Article in a conference publication
Event2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Tunis, Tunisia
Duration: 9 Mar 202011 Mar 2020

Conference

Conference2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020
CountryTunisia
CityTunis
Period9/03/2011/03/20

Abstract

In 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.

Keywords

  • 1D Convolutional Neural Networks, land use/land cover classification, multifrequency classification, Polarimetric Synthetic Aperture Radar (PolSAR)

Publication forum classification

Field of science, Statistics Finland