1 Introduction
2 Taxonomy of hyperspectral data analysis tasks
2.1 Land cover classification
2.2 Target/anomaly detection
2.3 Unmixing
2.4 Physical/chemical parameter estimation
3 Machine learning approaches
3.1 Types of learning
3.2 Non-probabilistic and probabilistic models
3.3 Bias and variance
3.4 Parametric and non-parametric models
3.5 Model selection and performance evaluation
3.6 Feature extraction and feature learning
4 Machine learning for hyperspectral analysis
5 Open Issues and Future Challenges
5.1 Curse of dimensionality
5.2 Robustness and reliability of models
5.3 Big data without ground truth
5.4 Lack of standardized datasets and experiments
6 Conclusion