Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification

Keyphrases

• Land Cover Classification 100%
• Hyperspectral 100%
• Producer's Accuracy 20%
• Generalized Reduced Gradient Method 20%
• Indexing Algorithms 20%
• Method Accuracy 20%
• Method Complexity 20%
• Performance Comparison 20%
• Overall Classification Accuracy 20%
• Data Mining Framework 20%
• Image Classification 20%
• Mathematical Formalism 20%
• Indiana 20%
• Waikato Environment for Knowledge Analysis 20%
• Random Forest Classifier 20%
• Gradient Optimization Algorithm 20%
• Generalized Reduced Gradient 20%
• Classification Process 20%
• Fractional Abundance 20%
• Special Characteristic 20%
• Pixel Purity Index 20%
• Image Data 20%

Computer Science

• Random Decision Forest 100%
• Support Vector Machine 100%
• Hyperspectral Image 100%
• Hyperspectral Imaging 40%
• Data Mining 20%
• Mathematical Formalism 20%
• Image Classification 20%
• Gradient Method 20%
• Random Forest Classifier 20%
• Classification Accuracy 20%
• Classification Process 20%
• Decision Maker 20%
• Optimization Algorithm 20%
• Performance Comparison 20%

Earth and Planetary Sciences

• Land Cover 100%
• Support Vector Machine 100%
• Hyperspectral Image 100%
• Hyperspectral Imaging 40%
• Data Mining 20%
• Indiana 20%
• Image Classification 20%