Detecting large-scale urban land cover changes from very high resolution remote sensing images using CNN-based classification

Keyphrases

• Geographic Information System 100%
• Pixel-based 100%
• High-resolution Remote Sensing Images (HRRSIs) 100%
• System Maps 100%
• Change Map 100%
• CNN-based 100%
• Urban Land Cover Change 100%
• Atrous Convolutional Neural Networks 100%
• Polygon 66%
• Land Cover Classification 66%
• Land Cover 66%
• Ground Resolution 66%
• Object-based Change Detection 66%
• Urban Areas 33%
• China 33%
• Aerial Image 33%
• Satellite Images 33%
• Change Detection 33%
• High-resolution Image 33%
• Robust Features 33%
• Encoder 33%
• Land Use Land Cover 33%
• Detection Accuracy 33%
• Wuhan 33%
• Developing Cities 33%
• Deep Learning Methods 33%
• Convolutional Neural Network 33%
• Atrous Convolution 33%
• Classification Map 33%
• Urban Land Cover 33%
• Classification Detection 33%
• Convolutional Layer 33%
• Image Current 33%

Earth and Planetary Sciences

• Land Cover Change 100%
• Land Cover 100%
• Urban Land Cover 100%
• Remote Sensing 100%
• Geographic Information System 75%
• Change Detection 75%
• China 25%
• Satellite Image 25%
• Land Use 25%