Comparing feature extraction techniques for urban land-use classification

Keyphrases

• Feature Vector 100%
• Feature Extraction Methods 100%
• Urban Land Use Classification 100%
• Most Important Problem 50%
• Principal Coordinate Analysis (PCoA) 50%
• Dimensionality Reduction 50%
• Classification Accuracy 50%
• Number of Features 50%
• Information Content 50%
• Multispectral Image 50%
• Pattern Classes 50%
• Artificial Neural Network Method 50%
• Specific Features 50%
• Discriminatory Power 50%
• Land Use Analysis 50%
• Digital Image Processing 50%
• Pattern Analysis 50%
• Self-organizing Feature Maps 50%
• Minimum number 50%
• Reliable Feature 50%

Engineering

• Feature Extraction 100%
• Dimensionality 100%
• Urban Land Use 100%
• Feature Vector 66%
• Land Use 33%
• Input Data 33%
• Image Processing 33%
• Classification Accuracy 33%
• Component Analysis 33%
• Principal Components 33%
• Image Data 33%
• Pattern Detection 33%
• Information Content 33%
• Multispectral Image 33%
• Artificial Neural Network 33%