Geng et al. (2026) Tri-CoMamba: A Tri-Complementary Mamba Framework for Multisource Remote Sensing Image Classification

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Zhihui Geng, Jiangtao Wang, Rui Wang
• DOI: 10.1109/jstars.2026.3662146

Research Groups

[Information not available in the provided text.]

Short Summary

This paper introduces Tri-CoMamba, a novel tri-complementary Mamba framework designed for the classification of multisource remote sensing images.

Objective

• To develop and evaluate a tri-complementary Mamba framework (Tri-CoMamba) for enhanced classification performance in multisource remote sensing imagery.

Study Configuration

• Spatial Scale: [Information not available in the provided text.]
• Temporal Scale: [Information not available in the provided text.]

Methodology and Data

• Models used: Tri-Complementary Mamba Framework (Tri-CoMamba), likely a deep learning architecture based on Mamba state-space models.
• Data sources: Multisource remote sensing images (specific types not detailed in the provided text).

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{Geng2026TriCoMamba,
  author = {Geng, Zhihui and Wang, Jiangtao and Wang, Rui},
  title = {Tri-CoMamba: A Tri-Complementary Mamba Framework for Multisource Remote Sensing Image Classification},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3662146},
  url = {https://doi.org/10.1109/jstars.2026.3662146}
}