@article{Lu2026, 
author = {Guangquan Lu and Shilong Lin and Shichao Zhang and Jiacheng Jiang and Yadan Han and Guoqiu Wen and Taotao Qiu},
title = {Efficient Channel Transformer for Processing Agricultural Aerial Images},
year = {2026},
journal = {Tsinghua Science and Technology},
keywords = {image segmentation, agriculture, multi-modal images},
url = {https://www.sciopen.com/article/10.26599/TST.2025.9010056},
doi = {10.26599/TST.2025.9010056},
abstract = {Existing methods always neglect the value of channel features in processing multi-modal agricultural aerial images with Near-Infrared (NIR) characteristics, as well as ignore the importance of channel features in fusion features that contain both downsampled low-level features and upsampled high-level features. Two modules are proposed in this paper to address this issue. For fusion features composed of spatial information, semantic information, and multi-modal information, a transformer-based channel feature enhancement module is first constructed to facilitate the recognition of fusion features located in different channels. The second module is Dual Cross-Entropy Dice (Dual-CE-Dice) loss, which can improve the phenomenon of class imbalance while helping the model to better learn channel features. Extensive experiments have been conducted on the Agriculture-Vision-2021 dataset and the Tianchi suichang-round1 dataset, proving that the proposed method Channel Transformer (CFormer) is superior to the previous methods.}
}