Classification of multispectral imagery using dynamic learning neural network

Keyphrases

• Adaptation Rules 33%
• Agriculture Area 33%
• Backpropagation 33%
• Bare Soil 33%
• Barren Soil 33%
• Built-up Land 33%
• Classification Accuracy 66%
• Classification Performance 33%
• Classification Results 33%
• Dynamic Learning 100%
• Feed Forward Net 33%
• High-resolution 33%
• Kalman Filter Algorithm 33%
• Land Cover 33%
• Land Cover Classification 33%
• Learning Networks 33%
• Learning Neural Network 100%
• Man-made 33%
• Multilayer Perceptron 33%
• Multispectral Imagery 100%
• Network Architecture 33%
• Neural Nets 33%
• Neural Network 33%
• Neural Network Training 33%
• Northern Taiwan 33%
• Pond Soil 33%
• Real Image 33%
• Simulated Images 33%
• Small Ponds 33%
• Soil-vegetation 33%
• Taoyuan County 33%
• Three-band 33%
• Timing Accuracy 33%
• Training Data 33%
• Training Process 33%
• Training Time 66%

Engineering

• Classification Accuracy 100%
• Classification Performance 50%
• Deep Learning Method 100%
• Feedforward 50%
• High Resolution 50%
• Input Node 50%
• Kalman Filter 50%
• Land Cover 100%
• Nodes 50%
• Output Node 50%
• Perceptron 50%
• Real Image 50%
• Test Site 50%

Earth and Planetary Sciences

• Kalman Filter 50%
• Land Cover 100%
• Neural Net 50%
• Self Organizing Systems 50%
• SPOT 50%
• Taiwan 50%
• Vegetation 50%