Hyperspectral Imaging

<p>1. INTRODUCTION<br>1.1. Hyperspectral Images. From Remote sensing to bench top instruments. A general overview<br>1.2. Hyperspectral cameras. Types of hyperspectral cameras, radiations and sensors</p> <p>2. ALGORITHMS AND METHODS<br>2.1. Pre-processing of hyperspectral images. Spatial and spectral issues<br>2.2. Hyperspectral data compression<br>2.3. Pansharpening<br>2.4. Unsupervised pattern recognition methods<br>2.5. Multivariate Curve Resolution<br>2.6. Non Linear Spectral un-mixing<br>2.7. Variability of the endmembers in spectral unmixing<br>2.8. Regression models<br>2.9. Classical Least Squares for Detection and Classification<br>2.10. Supervised Classification Methods in Hyperspectral Imaging - Recent Advances<br>2.11. Fusion of Hyperspectral Imaging and LiDAR for Forest Monitoring<br>2.12. Hyperspectral time series analysis: Hyperspectral image data streams interpreted by modeling known and unknown variations<br>2.13. Statistical Biophysical Parameter Retrieval and Emulation with Gaussian Processes</p> <p>3. APPLICATION FIELDS<br>3.1. Hyperspectral cameras adapted to the applications. How and when<br>3.2. Applications in Remote Sensing - Natural Landscapes<br>3.3. Applications in Remote Sensing - Anthropogenic activities<br>3.4. Vegetation and crops<br>3.5. Food and feed production<br>3.6. Hyperspectral Imaging for Food related Microbiology Applications<br>3.7. Hyperspectral Imaging in Medical Applications<br>3.8. Hyperspectral Imaging as a part of Pharmaceutical Product Design<br>3.9. Hyperspectral imaging for artworks investigation<br>3.10. Growing applications of hyperspectral and multispectral imaging</p>