Optical & Remote Sensing
This three-day short course reviews the underlying technology areas used to construct and operate space-based optical sensors, laser and radar systems. The course presents background information to allow an appreciation for designing and evaluating space-based sensing systems. The course provides a broad introduction to a wide range of optical sensing systems with specific examples. Fundamental descriptions are given for various optical sensing systems, and, details associated with space applications are presented. System requirements are developed and methodology of system component selection is given. Design considerations for space-based optical sensors are discussed and case studies describing previous and current space instrumentation are presented. Example systems will be discussed, along with applications and future directions.
What you will learn:
- What are the fundamentals of optical remote sensing.
- Sensors and detectors for optical remote sensing.
- Active and passive microwave systems.
- LiDAR systems, data and data processing
- End to end data acquisition and processing.
- Optical data, data handling and data formats.
- Calibration and pre-processing of optical data
- Integration of optical remote sensing data with ancillary data in a Geomatics and Geographic Information System
- Future directions and advances.
- Where the most promising international research is being performed.
- Introduction. The fundamentals of remote sensing, remote sensing sensors, detectors, the electromagnetic spectrum, characteristics of space remote sensing systems.
- The History and Origins of Space Remote Sensing. The origins of space remote sensing, the origins, history and current state of the Canadian remote sensing community, dual use issues, ISS systems, the remote sensing process, remote sensing sensor design and development, visible and IR sensing, passive electro-optical systems, multispectral and hyperspectral sensing, international organizations and structures, remote sensing satellite orbits, etc.
- Optical Remote Sensing Sensors. Sensors and detectors, electromagnetic spectrum, Wien’s displacement law, Planck’s general equation, quantum photons, types of sensors, radiant energy, flux and intensity and radiance, scanner designs, single detectors, pushbroom and two dimensional arrays, framing and scanning systems, cross track and along track sensors, instantaneous field of view, optical vs. microwave, passive vs active sensors, radiometers, spectrometers, and imaging sensors, spatial, radiometric, temporal and spectral resolution, the electromagnetic energy budget, ultra-high resolution systems, etc.
- LiDAR Systems. The fundamentals of LiDAR, laser remote sensing, pulsed and continuous wave systems, history and development, UV, visible and Near IR systems, airborne and space systems, LiDAR applications, data processing and unique data analysis and processing issues, creating Digital Elevation Models (DEMs) with LiDAR systems, space systems and applications, CMOS and hybrid CMOS/CCD systems, atmospheric and meteorology, Doppler LiDAR and Rayleigh Doppler LiDAR systems, scanning LiDAR systems.
- Microwave Systems-Passive and Active. The fundamentals of microwave remote sensing, passive vs active microwave sensing, microwave sensing design and considerations, SLAR image geometry, incidence angle, scattering mechanisms and specular reflectance, scene illumination, radar bands, layover and foreshortening, dielectric constant, polarization, interferometry, differences between active and passive data, data analysis and data processing, case studies of Canadian RADARSAT, RADARSAT Constellation, and TerraSAR-X, future systems.
- Calibration, Noise, Pre-processing and Processing of Optical Remote Sensing Data The end-to-end data processing chain, sensor signal processing, FFT, digital numbers (DNs), data transmission, data calibration, atmospheric scattering and absorption, image restoration, remote sensing data structure and data formats, metadata, data pre-processing, data calibration, atmospheric calibration, geometric registration, coordinate transformations, data processing, modular transfer functions, spatial filters, temporal analysis and time series modeling, thematic classifications, supervised and unsupervised classifications, spectral signatures, accuracy assessment, data fusion, references.
- Applications. Space and airborne remote sensing applications, local, regional and global applications, land, water and atmospheric applications
- Integration of Data within the Geomatics and GIS Context. Integration of data within the GIS context, data fusion, geomatics, fundamentals of GIS, integration with vector and GNSS point data, the multi-concept, GIS data modeling, final data analysis and data presentation, data archiving and metadata.
- Current Status and Future Directions. Future directions for optical remote sensing systems, sensors, data and data processing. New systems such as Planet Labs and Google’s Sky-Box satellites.
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Prof. Scott Madry has worked in the fields of satellite remote sensing and applications for the past thirty years. He is on the faculty of the University of North Carolina at Chapel Hill and also the International Space University in Strasbourg, France. His research focuses on the regional applications of integrated space remote sensing, GNSS, and Geographic Information Systems data for environmental and cultural resource management and disaster planning and response. He has given over 150 short courses and workshops in over 30 countries around the world on these topics and he has done field work in North America, Asia, Africa and Europe. He has published widely on these subjects, and is co-editor of the recently published 1,228 page Handbook of Satellite Applications by Springer Press. He is an engaging and entertaining lecturer with a broad grasp of the interconnections between disciplines and applications.
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