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Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese
Wancheng Tao1,2, Yi Dong1,2, Wei Su1,2
1College of Land Science and Technology, China Agricultural University, Beijing, China.
Frontiers in Plant Science
|July 8, 2022
Summary
Accurate corn residue classification is crucial for conservation tillage. A new multi-scale feature fusion and 1D-CNN-CA method using GF-2 PMS images achieved 97.26% accuracy, improving soil protection monitoring.
Area of Science:
- Agricultural remote sensing
- Soil science
- Image processing
Background:
- Crop residue management is key to conservation tillage, protecting black soil by reducing erosion and increasing soil organic carbon.
- Accurate classification of corn residue cover is vital for effective monitoring of agricultural practices.
- High-resolution remote sensing offers objective regional assessment, but faces challenges like intra-object heterogeneity and spectral confusion.
Purpose of the Study:
- To develop and validate a multi-scale feature fusion and classification method for accurate corn residue-covered area classification using GF-2 PMS images.
- To address the challenges of intra-object heterogeneity and spectral confusion in high-resolution remote sensing data.
- To improve the monitoring of crop residue management for enhanced soil conservation.
Main Methods:
- Multi-scale image features were generated using wavelet transform and principal component analysis (PCA) to reduce heterogeneity.
- An optimal image dataset (OID) was identified by comparing feature fusion models.
- A 1D convolutional neural network with an attention mechanism (1D-CNN-CA) was employed for classification.
Main Results:
- The 1D-CNN-CA method, utilizing multi-scale features, achieved the highest classification accuracy (Kappa: 96.92%, Overall Accuracy: 97.26%).
- This approach effectively mitigated intra-object heterogeneity and spectral confusion present in the high-resolution imagery.
- Multi-scale image features demonstrated superiority in classifying corn residue cover compared to other features.
Conclusions:
- The proposed 1D-CNN-CA method combined with multi-scale feature fusion provides a highly accurate and effective approach for classifying corn residue-covered areas.
- This technique enhances the visualization and classification of residue cover types, supporting better soil management decisions.
- The study highlights the importance of advanced feature extraction and deep learning models for remote sensing applications in agriculture.
