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Crop classification method for multi-temporal remote sensing imagery based on a (3 + 2)D SAFPN.

Yicong Sun1, Tingting Zhao1, Yue Zhang1

  • 1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.

Frontiers in Plant Science
|February 26, 2026
PubMed
Summary

A new (3+2)D Split-Attention Feature Pyramid Network ((3+2)D SAFPN) improves crop classification using remote sensing data. This advanced model enhances agricultural monitoring and food security by accurately mapping crop types.

Keywords:
crop classificationdeep learningfeature pyramid networkmulti-temporal parcelsremote sensing

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Area of Science:

  • Agricultural remote sensing
  • Computer vision for agriculture
  • Geospatial data analysis

Background:

  • Accurate crop classification is vital for agricultural monitoring and global food security.
  • Effectively utilizing spatiotemporal information from multi-temporal remote sensing data is a significant challenge in crop mapping.
  • Existing methods often struggle to integrate diverse data sources for comprehensive crop analysis.

Purpose of the Study:

  • To propose an improved neural network model, the (3+2)D Split-Attention Feature Pyramid Network ((3+2)D SAFPN), for enhanced crop classification.
  • To effectively integrate spatiotemporal dynamics, multi-scale spatial features, and inter-channel information for robust crop mapping.
  • To address the challenge of learning performance on minority crop classes through a focal loss function.

Main Methods:

  • Development of a hybrid (3+2)D Feature Pyramid Network (FPN) integrating 3D FPN for spatiotemporal dynamics and 2D FPN for spatial features.
  • Incorporation of a split-attention (SA) mechanism to improve inter-channel information interaction.
  • Utilization of a focal loss function to enhance learning for minority crop classes.
  • Construction of a plot-level NDVI time-series dataset from multi-temporal Sentinel-2 imagery (2024) for Inner Mongolia, China.

Main Results:

  • The proposed (3+2)D SAFPN model achieved high accuracies of 89.01% (test set) and 89.06% (validation set), with Kappa coefficients of 0.82.
  • The model outperformed the original (3+2)D FPN baseline, demonstrating improved performance in crop classification.
  • Experiments on the public Munich dataset showed strong generalization ability, with accuracy improvements of 2.88% (test) and 2.44% (validation).

Conclusions:

  • The (3+2)D SAFPN model effectively integrates spatial, spectral, and temporal information for robust and high-accuracy crop classification.
  • This approach offers a promising solution for large-scale agricultural monitoring and contributes to food security assurance.
  • The developed model demonstrates significant potential for practical applications in precision agriculture and land cover mapping.