Syllabus
Image processing : Image registration – definition principle and procedure – Fundamentals of image recertification, interpolation- intensity interpolation- Radiometric & geometric correction of remotely sensed data. Basic statistical concept in DIP and use of probability methods in DIP- Image enhancement techniques – an overview-Contrast enhancement – linear and nonlinear, histogram equalisation and density slicing Spatial filtering and edge enhancement, Multi image manipulation – addition, subtraction and band rationing -Enhancement by using colours – advantages, types of colour enhancements
Supervised (maximum likelihood & Random Forest ) and unsupervised (ISODATA & k-mean) image classification. classification. Integration of artificial intelligence techniques with geospatial data to extract meaningful insights. It will include pattern detection, change detection, prediction, and spatiotemporal forecasting
RADAR Techniques: SAR Interferometry (InSAR, DInSAR) and Polarimetry: [ fundamental concept, methodology, processing, application], SAR Systems and Image Acquisition Modes, SAR data processing and backscatter image generation, Advance techniques of SAR Remote Sensing, Application of SAR imagery in the field of defence and security; Fundamentals of RADAR, SAR Interferometry, and SAR imagery; Introduction to SAR sensors and platforms, SAR geometrical and radiometric effects, enhancements of a SAR image, basic SAR imagery ordering, interpretation of SAR imagery, SAR signatures, change detection using amplitude and interferometry coherence map, SAR interferometry ordering, coherence maps, DEM generation, interferogram and displacement maps SAR interferometry applications in the field of security and defence; Applications of RADAR -soil response-vegetation response- water and ice response- urban area response
LiDAR: Measurements using LiDAR and its applications: temporal and spatial coverage, Impact of Errors, Information extraction from LiDAR data, Principles of LiDAR, LiDAR sensors and platforms, LiDAR data view, processing, and analysis, LiDAR applications: topographic mapping, vegetation characterization, and 3-D modeling of urban infrastructure, Basic skills of LiDAR needed to leverage the commercial LiDAR sources, Software packages (ArcGIS LAS Dataset; FUSION/LDV; PointVue LE; LAStools) for LiDAR data displaying, processing, and analyzing. LIDAR data applications
Hyper-spectral Remote Sensing: Hyper-spectral Imaging: Hyper spectral concepts, data collection systems, calibration techniques, data processing techniques; preprocessing, N-dimensional scatter-plots, Special angle mapping, Spectral mixture analysis, Spectral Matching, Mixture tuned matched filtering, Classification techniques, airborne and space-borne hyperspectral sensors, applications. High resolution hyperspectral satellite systems: Sensors, orbit characteristics, description of satellite systems, data processing aspects, applications.
Skills acquired : Theoretical and practical knowledge of acquiring and processing RADAR, LIDAR and hyperspectral data.