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  1. Home
  3. Crop Classification In The Middle Reaches Of The Hei River Based On Model Transfer.
  1. Home
  3. Crop Classification In The Middle Reaches Of The Hei River Based On Model Transfer.

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Crop classification in the middle reaches of the Hei River based on model transfer.

Huazhu Xue1, Yongkang Fan1, Guotao Dong2,3

  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China.

Scientific Reports
|November 23, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a method for crop classification in sample-free years using multisource spectral data (MSSD). Fine-tuning models with MSSD significantly improves accuracy, reducing the need for extensive labeled samples in remote sensing applications.

Keywords:
Crop classificationDeep learningModel transferSpectral library

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Area of Science:

  • Remote Sensing
  • Agricultural Science
  • Data Science

Background:

  • Crop classification is vital for agricultural resource monitoring and water management, particularly in arid regions.
  • Acquiring sufficient labeled samples for crop classification is resource-intensive and may not be feasible for all years.

Purpose of the Study:

  • To develop a crop classification method for sample-free years in the Hei River basin.
  • To assess the effectiveness of using multisource spectral data (MSSD) for model fine-tuning.

Main Methods:

  • Generated multisource spectral data (MSSD) using a spectral library and existing sample data.
  • Pre-trained deep learning (CNN) and machine learning (RF) models with labeled samples.
  • Fine-tuned the pre-trained models using MSSD for crop classification in years lacking sample data.

Main Results:

  • Fine-tuning models with MSSD achieved accurate crop classification, with overall accuracy exceeding 90% in model transfer experiments.
  • The approach significantly enhances classification accuracy when labeled sample data is limited.
  • This method reduces the dependency of deep learning models on large-scale sample datasets.

Conclusions:

  • Fine-tuning models with MSSD is an effective strategy for crop classification in data-scarce years.
  • The developed method supports agricultural resource utilization and policy formulation in the Hei River basin.
  • This approach offers a viable solution for overcoming limitations in sample data availability for remote sensing-based crop classification.