Ahishali M.Kiranyaz S.İnce, TürkerGabbouj, Moncef2023-06-162023-06-1620209.78E+12https://doi.org/10.1109/M2GARSS47143.2020.9105312https://hdl.handle.net/20.500.14365/3579The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (IEEE GRSS)2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 -- 9 March 2020 through 11 March 2020 -- 160621In 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. © 2020 IEEE.eninfo:eu-repo/semantics/closedAccess1D Convolutional Neural Networksland use/land cover classificationmultifrequency classificationPolarimetric Synthetic Aperture Radar (PolSAR)Computational efficiencyConvolutionConvolutional neural networksGeologyImage classificationLand useOne dimensionalRadar imagingRemote sensingClassification accuracyClassification approachClassification performanceLand use/land coverMulti frequencyMultiple frequencyPolarimetric SARSingle bandClassification (of information)Conference Object10.1109/M2GARSS47143.2020.91053122-s2.0-85086740246