一种基于深度学习的玉米种植面积提取方法及系统
By fusing radar and optical image data using deep learning methods, multiple vegetation indices and polarization feature indices are constructed. Temporal convolutional networks are used to extract phenological features, solving the problem of limited accuracy and efficiency in maize planting area extraction in existing technologies, and achieving high-precision maize planting area calculation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN YIMIJING TECH CO LTD
- Filing Date
- 2026-05-12
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for extracting corn planting area rely on manual rules and shallow classifiers, which are difficult to adapt to complex agricultural environments. Furthermore, cloud computing platforms cannot natively support deep learning, resulting in limited extraction accuracy and efficiency.
A deep learning-based approach was adopted to construct multiple vegetation indices and polarization feature indices by fusing radar and optical image data. Temporal convolutional networks combined with temporal attention mechanisms were used to extract phenological features, construct a multidimensional classification feature matrix, identify maize planting areas, and perform connected component analysis and hole filling.
It significantly improves the accuracy of maize planting area identification and the reliability of area calculation, providing strong support for maize planting monitoring and management.
Smart Images
Figure CN122176039B_ABST