Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing

1. Introduction.- 2. Machine Learning Methods for Spatial and Temporal Parameter Estimation.- 3. Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms.- 4. Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine.- 5. Advances in Deep Learning for Hyperspectral Image Analysis - Addressing Challenges Arising in Practical Imaging Scenarios.- 6. Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.- 7. Supervised, Semi Supervised and Unsupervised Learning for Hyperspectral Regression.- 8. Sparsity-based Methods for Classification.- 9. Multiple Kernel Learning for Hyperspectral Image Classification.- 10. Low Dimensional Manifold Model in Hyperspectral Image Reconstruction.- 11. Deep Sprase Band Selection for Hyperspectral Face Recognition.- 12. Detection of Large-Scale and Anomalous Changes.- 13. Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning.- 14. Chapter Hyperspectral-Multispectral Image Fusion Enhancement Based on Deep Learning.- 15. Automatic Target Detection for Sparse Hyperspectral Images.