Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Images

Keyphrases

• Machine Learning Techniques 100%
• Synthetic Aperture Radar Image 100%
• X-band 100%
• Phenology 100%
• Classification Schema 100%
• Paddy Rice Phenology 100%
• Classification Basis 100%
• Agricultural Area 50%
• Turkey 50%
• Machine Learning Algorithms 50%
• Spain 50%
• Support Vector Machine 50%
• Classification Algorithms 50%
• High Demand 50%
• All-weather 50%
• High Temporal Resolution 50%
• Morphological Changes 50%
• Thematic Maps 50%
• Textural Features 50%
• Crop Monitoring 50%
• Growth Cycle 50%
• Synthetic Aperture Radar Sensors 50%
• Polarimetric Features 50%
• Space Sensor 50%
• Decision Tree 50%
• Rice 50%
• Paddy Rice 50%
• Class number 50%
• Satellite Systems 50%
• TerraSAR-X Images 50%
• Crop Phenology 50%
• Linear Kernel 50%
• Phenology Monitoring 50%
• Nonlinear Kernels 50%
• K-nearest 50%
• Neighbor-joining Tree 50%
• Acquisition Capability 50%

Earth and Planetary Sciences

• Synthetic Aperture Radar 100%
• Machine Learning 100%
• Farmland 50%
• TerraSAR-X 50%
• Morphology 50%
• Support Vector Machine 50%

Agricultural and Biological Sciences

• Synthetic Aperture Radar 100%
• Phenology 100%
• Paddies 100%
• Support Vector Machine 20%
• Thematic Map 20%