An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

作者：

Highlights：

• A novel supervised domain adaptation framework SDACD for cross-domain change detection.

• SDACD unifies image adaptation and feature adaptation in an end-to-end trainable manner.

• SDACD can handle cross-domain change detection and consistently improve the performance as an easy-to-plug-in module.

• Our SNUNet-based framework sets new state-of-the-art performance with an F1-score of 97.34% on CDD dataset and 92.36% on WHU building dataset.

摘要

•A novel supervised domain adaptation framework SDACD for cross-domain change detection.•SDACD unifies image adaptation and feature adaptation in an end-to-end trainable manner.•SDACD can handle cross-domain change detection and consistently improve the performance as an easy-to-plug-in module.•Our SNUNet-based framework sets new state-of-the-art performance with an F1-score of 97.34% on CDD dataset and 92.36% on WHU building dataset.

论文关键词：Change Detection,Supervised Domain Adaptation,Image Adaptation,Feature Adaptation

论文评审过程：Received 31 March 2022, Revised 1 July 2022, Accepted 7 August 2022, Available online 11 August 2022, Version of Record 17 August 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108960