Li et al. (2026) A Pseudomultitask Neural Network Classification Model for Cropland Mapping in Mountainous Areas Using High-Resolution Remote Sensing Images

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Jianhui Li, Zhaoming Zhou, Min Deng, Xiaobing Zhou, Jianzhong Chen
• DOI: 10.1109/jstars.2026.3667717

Research Groups

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Short Summary

This paper introduces a pseudomultitask neural network classification model specifically designed for accurate cropland mapping in challenging mountainous regions, utilizing high-resolution remote sensing imagery.

Objective

• To develop and apply a pseudomultitask neural network classification model for improved and precise cropland mapping in mountainous areas using high-resolution remote sensing images.

Study Configuration

• Spatial Scale: Mountainous areas. Specific geographic extent not provided.
• Temporal Scale: Not provided in the input text.

Methodology and Data

• Models used: Pseudomultitask Neural Network Classification Model.
• Data sources: High-Resolution Remote Sensing Images. Specific sensors or platforms not provided.

Main Results

Quantitative results and specific findings are not provided in the input text.

Contributions

Specific contributions beyond the development and application of the described model for cropland mapping in mountainous areas are not provided in the input text.

Funding

Not provided in the input text.

Citation

@article{Li2026Pseudomultitask,
  author = {Li, Jianhui and Zhou, Zhaoming and Deng, Min and Zhou, Xiaobing and Chen, Jianzhong},
  title = {A Pseudomultitask Neural Network Classification Model for Cropland Mapping in Mountainous Areas Using High-Resolution Remote Sensing Images},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3667717},
  url = {https://doi.org/10.1109/jstars.2026.3667717}
}