TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

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

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Proceedings
KustantajaIEEE
Sivut73-76
Sivumäärä4
ISBN (elektroninen)9781728121901
DOI - pysyväislinkit
TilaJulkaistu - 1 maaliskuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Tunis, Tunisia
Kesto: 9 maaliskuuta 202011 maaliskuuta 2020

Conference

Conference2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020
MaaTunisia
KaupunkiTunis
Ajanjakso9/03/2011/03/20

Tiivistelmä

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.