Classification approach | Algorithm | Advantages | Disadvantage | Reference |
Pixel-Based | Artificial Neural Networks | Manage well large feature space; Indicate strength of class membership; Resistant to training data deficiencies-requires less training data than DT | Needs parameters for network design; Tends to overfit data; Black box (rules are unknown); Computationally intense; Slow training | [46] |
Sub-pixel Based | Spectral Unmixing | Clear physical meaning and being able to estimate fractional distribution | Hard to find a proper endmember in larger scale | [60] |
Object Based | Support Vector Machines (SVM) | Manages well large feature space; Insensitive to Hughes effect; Works well with small training dataset and does not overfit | Needs parameters: regularization and kernel; Poor performance with small feature space; Computationally intense | [46] |