Emerging Computing Paradigms

Learning Results

By the end of the course, students will have understood the following:

• Basic concepts of Electro-Magnetic radiation and its mechanisms of interaction with matter and its propagation through the atmosphere
• Description and analysis of digital Remote Sensing data: multi- and hyper-spectral data and radar images
• Types of satellite systems and earth monitoring programs
• Procedures for the recognition, pre-processing and improvement-correction of digital images: radiometric and atmospheric correction
• Satellite image classification: methods and techniques
• Machine learning algorithms in Remote Sensing
• Machine learning applications in Remote Sensing: Monitoring of urban and rural environment changes, Land use, Object detection, Disaster monitoring
• Presentation of specialized satellite image analysis software

Skills acquired

• Retrieve, analyse and synthesise data and information, with the use of necessary technologies
• Work individually and in teams
• Advance free, creative and causative thinking

Basic principles of Electro/Magnetic (E/M) radiation (laws, interactions of electromagnetic radiation with the atmosphere and the surface of the Earth, spectral signatures). Processing of satellite images (Geometric deformations, geo-reference, atmospheric and radiometric correction). Filters and indicators. Presentation and description of digital remote sensing data (multi- and super-spectral sensors, visible/infrared). Landsat, Worldview, Sentinel, Copernicus programme. Thematic information extraction. Classification of satellite images (unsupervised, supervised, object-oriented). Expert systems. Decision Trees. Machine Learning approaches in Remote Sensing. Applications: Monitoring of urban and rural environment changes, Land use, Agriculture, Object detection, Disaster monitoring. Presentation of the use of specialized remote sensing software (ENVI & SNAP). Analysis and description of multispectral image data. Histogram, geometric, radiometric and atmospheric corrections, methods of improvement. Types of image classification, machine learning applications.

1. Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing. Guilford Press.

2. Cracknell, A. P. (2007). Introduction to remote sensing. CRC press.

3. Jensen, J. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective, Pearson Press.

4. Lillesand, T, Kiefer, R.W., Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.

5. Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1), 3-10.

6. Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784-2817.