Yang et al. (2026) AMD-DSMNet: Asymmetric Multidirectional Convolutional Attention and Dynamic Spatial Modulation Network for Cropland Change Detection

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Xin Yang, Yurong Qian, Palidan Tuerxun, Lu Bai, Yuanxu Wang, Ying Fan, Yujiang He, Weijun Gong
• DOI: 10.1109/jstars.2026.3663177

Research Groups

Not available in the provided text.

Short Summary

This paper introduces AMD-DSMNet, a novel deep learning architecture featuring asymmetric multidirectional convolutional attention and dynamic spatial modulation, designed for the task of cropland change detection. The main findings are not available in the provided text.

Objective

• To develop and evaluate AMD-DSMNet, an Asymmetric Multidirectional Convolutional Attention and Dynamic Spatial Modulation Network, for effective cropland change detection.

Study Configuration

• Spatial Scale: Not available in the provided text.
• Temporal Scale: Not available in the provided text.

Methodology and Data

• Models used: AMD-DSMNet (Asymmetric Multidirectional Convolutional Attention and Dynamic Spatial Modulation Network).
• Data sources: Not available in the provided text.

Main Results

Not available in the provided text.

Contributions

• Introduction of AMD-DSMNet, a new network architecture for cropland change detection.
• Integration of asymmetric multidirectional convolutional attention and dynamic spatial modulation within the network design.

Funding

Not available in the provided text.

Citation

@article{Yang2026AMDDSMNet,
  author = {Yang, Xin and Qian, Yurong and Tuerxun, Palidan and Bai, Lu and Wang, Yuanxu and Fan, Ying and He, Yujiang and Gong, Weijun},
  title = {AMD-DSMNet: Asymmetric Multidirectional Convolutional Attention and Dynamic Spatial Modulation Network for Cropland Change Detection},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3663177},
  url = {https://doi.org/10.1109/jstars.2026.3663177}
}