Wang et al. (2026) Dual-Consistency Input-Space Domain Adaptation for Remote Sensing Image Semantic Segmentation via SSC-CycleGAN

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Longbao Wang, Yiding Ma, Meng Ding, Yinqi Luan, Shun Luo, Xiaoyang Meng, Yueyang Mao, Hui Gao
• DOI: 10.1109/jstars.2026.3674122

Research Groups

[Information not available in the provided text.]

Short Summary

This paper introduces a dual-consistency input-space domain adaptation method, utilizing an SSC-CycleGAN, to improve semantic segmentation performance for remote sensing images by addressing domain shift.

Objective

• To develop and apply a dual-consistency input-space domain adaptation technique (SSC-CycleGAN) for enhancing semantic segmentation accuracy in remote sensing imagery, particularly across different data domains.

Study Configuration

• Spatial Scale: Various, pertaining to remote sensing imagery.
• Temporal Scale: [Information not available in the provided text.]

Methodology and Data

• Models used: SSC-CycleGAN (a variant of Generative Adversarial Networks).
• Data sources: Remote sensing images.

Main Results

[Information not available in the provided text.]

Contributions

[Information not available in the provided text.]

Funding

[Information not available in the provided text.]

Citation

@article{Wang2026DualConsistency,
  author = {Wang, Longbao and Ma, Yiding and Ding, Meng and Luan, Yinqi and Luo, Shun and Meng, Xiaoyang and Mao, Yueyang and Gao, Hui},
  title = {Dual-Consistency Input-Space Domain Adaptation for Remote Sensing Image Semantic Segmentation via SSC-CycleGAN},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3674122},
  url = {https://doi.org/10.1109/jstars.2026.3674122}
}