This course shall explain the fundamentals of microwave remote sensing in passive and active domain with application in civil engineering (hydrology, meteorology). An emphasis is made to teach microwave image processing through open source programming languages like python. Further objective of this course is to make a student capable to interpreting and deciphering the microwave data for complex scientific and policy problems in the water arena, all of which require high quality training.INTENDED AUDIENCE:Researchers, UG and PG students of Engineering, Remote Sensing, Industries working in Microwave remote sensing, environment and climate domain, Academicians working in Water resources and hydrology. PREREQUISITES:NilINDUSTRY SUPPORT:RMSI, ERDAS and similarly all firms/companies which have a remote sensing and GIS applications wing shall recognize/value this course
Overview
Syllabus
Week 1: Fundamentals of Electromagnetic Waves, Introduction to microwave remote sensing, Overview of non-imaging and imaging microwave sensors, principles, physical fundamentals, Installation of python using Anaconda Environment and basic commandsWeek 2: Scattering of Microwaves, Fundamentals of Synthetic Aperture Radar (SAR), Basics of Image formation, Basics of SAR Image processing using pythonWeek 3: Radar equation, Image defects - Geometric distortions, Introduction to Sentinel Application Platform (SNAP)Week 4: Speckle, Doppler Shift in SAR Imagery, Multilooking, Spatial Convolution, Introduction to plotting and image statistics in pythonWeek 5: Introduction to Texture, GLCM, Introduction to Image statistics in PythonWeek 6: Radar remote sensing, Speckle filtering using pythonWeek 7: Image classification, geometrical basis, Supervised Classification, SAR Image Classification using SNAPWeek 8: Unsupervised classification, Accuracy Assessment, Fuzzy Classification, Handling Active microwave data in PythonWeek 9: Active microwave remote sensing: Principles, Application of active microwave remote sensing in hydrology, Doppler weather radar data visualizationWeek 10: Radar Altimetry, concepts and applications in hydrology, Measuring soil moisture using active microwave remote sensing, Fundamentals of Passive microwave remote sensing and data handling using pythonWeek 11: Applications of passive microwave remote sensing in hydrology, Handling Precipitation data in pythonWeek 12: Radar Interferometry, using phase as a relative distance measure, Digital Elevation Models, Hydrological Models – An Introduction
Taught by
Prof.J. Indu
