PolSAR Terrain Classification Based on Transformer-Unet Hybrid Model in the Complex Domain

The Transformer model has recently achieved success in image classification tasks. Additionally, the polarimetric synthetic aperture radar (PolSAR) terrain classification has been confirmed to achieve better classification results in the complex domain compared to the real domain. Therefore, this pa...

Full description

Saved in:
Bibliographic Details
Published inIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium pp. 11123 - 11126
Main Authors Xie, Wen, Zhang, Jiapeng, Zhang, Zhezhe, Shan, Chenchao
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2024
Subjects
complex domain
Computational modeling
Computer architecture
Data mining
Feature extraction
Geoscience and remote sensing
Polarimetric synthetic aperture radar
Polarimetric synthetic aperture radar (PolSAR)
Transformer
Transformers
Unet
Online AccessGet full text

Cover

More Information
Summary:The Transformer model has recently achieved success in image classification tasks. Additionally, the polarimetric synthetic aperture radar (PolSAR) terrain classification has been confirmed to achieve better classification results in the complex domain compared to the real domain. Therefore, this paper introduces the Transformer into the complex domain and proposes a new classification network which named complex-valued Transformer-Unet hybrid model (CT-Unet), for PolSAR terrain classification. Its hybrid approach is to embed Transformer module into the U-Net encoder section for feature extraction on PolSAR data. The experimental results on PolSAR terrain classification using the Xi'an dataset demonstrate that our proposed model effectively improves the accuracy of PolSAR terrain classification.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10640599