Lu et al. (2025) Postprocessing for Fine Classification of Crops in UAV Hyperspectral Imagery

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-11-21
• Authors: Qikai Lu, Yanyan Tian, Pei Sun, Shaohua Liu, Lifei Wei
• DOI: 10.1109/jstars.2025.3635577

Research Groups

[Not available in the provided text.]

Short Summary

This paper focuses on developing postprocessing techniques to enhance the fine classification of crops using hyperspectral imagery acquired by Unmanned Aerial Vehicles (UAVs).

Objective

• To develop and evaluate postprocessing methods for improving the accuracy and fineness of crop classification based on UAV-borne hyperspectral imagery.

Study Configuration

• Spatial Scale: Field-level or farm-level (inferred from "UAV").
• Temporal Scale: Not specified, likely related to crop growth stages or a specific growing season.

Methodology and Data

• Models used: Postprocessing algorithms for classification (specifics not provided).
• Data sources: UAV-borne hyperspectral imagery.

Main Results

[Not available in the provided text.]

Contributions

[Not available in the provided text.]

Funding

[Not available in the provided text.]

Citation

@article{Lu2025Postprocessing,
  author = {Lu, Qikai and Tian, Yanyan and Sun, Pei and Liu, Shaohua and Wei, Lifei},
  title = {Postprocessing for Fine Classification of Crops in UAV Hyperspectral Imagery},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3635577},
  url = {https://doi.org/10.1109/jstars.2025.3635577}
}