Processing remote sensing Images
Objectives and Contextualisation
At the end of the course, the student will be able to:
Master different tools primary processing of aerial and satellite imagery.
Dominate the physical principles that govern remote image capture and transformations of the content of the image itself.
Distinguish the different sources of image geometric deformations and possible signal interference caused by atmospheric captured or lighting effects (topography, etc.).
Correctly apply the methodologies to mitigate the different error sources in order to be able to view and extract physical parameters of the received data.
Content
PHYSICAL PRINCIPLES OF REMOTE SENSING
Solar spectrum
Concepts: radiation and electromagnetic spectrum, polarization. Fundamental relationships between frequency, length and transported wave energy.
Basic physical parameters (terminology and symbology, definitions, units): Radiant energy, energy flow, energy intensity, radiance energy excitance, irradiance, reflectance, albedo, transmittance, absorptance; absorbance. spectral magnitudes.
Specular reflection, diffuse and lambertiana.
Black body (Planck's law, Stefan-Boltzmann law, Wien's displacement law).
Solar radiation. Exoatmospheric characteristics and the surface of the Earth; interaction with the atmosphere and atmospheric windows.
Spectral signatures. Main characteristics of water, soil and rocks and vegetation in the visible and infrared non thermal.
Factors that influence the spectral signature.
Thermal
The thermal radiation emitted by the Earth. Remote Sensing approaches.
Physical parameters of the thermal infrared region.
KCL. black body, white body and gray body. selective radiators. Thermal behaviour of an object-related parameters.
Thermal behavior of an object: related parameters.
Spectral behaviour of the different coverages in the thermal infrared region.
Factors which influence the emissivity.
Emissivity measurement. Field measurements.
Emissivity measurement. Measured from satellite.
Active microwave
Active Microwave Remote Sensing: Imaging Radar.
Wave-Matter interaction: Radar Cross Section and Backscattering Coefficient.
Backscattering Coefficient.
Backscattering models.
SAR polarimetry.
Passive microwave
Passive Sensors: Fundamentals andPhysical Principles.
Applications of passive microwave E.O.
Microwave Radiometers:
Figures of Merit: Angular Resolution and Radiometric Resolution.
Calibration: internal, external, use of multi-look information.
Present and future EO Passive Microwave Mission.
GEOMETRIC CORRECTION OF AERIAL AND SATELLITE IMAGERY
Geometric corrections. Deformation sources. Orthoimage, orthophoto and orthophoto of authentic orthophotomap concepts. Corrections in vectorial bases.
Physical models (collinearity equations orbit models), semi-empirical (polynomial corrections, models of rational functions, Delaunay triangulation) and mixed. Model of radar images: determining the sampling step azimuth and distance. Relief role. Ground control points (GCP), test points, homologous points.
Geometry of the radar image. Sampling of the image. Geometric distortion of images. Accurate geocoding images using Digital Elevation Models (DEM or DEM). Obtaining DEM and Radar Mapping. Approaches to areas of low relief. Examples.
Basic correction process. Nearest neighbor, bilinear and bicubic interpolation: Chromatic, radiometric and geometric in image resampling. Considerations about output pixel size.
Sources of GCP. Automatic GCP.
Basics of physical models. Consideration of the relief.
Basics of semi-empirical models:
Polynomial models 1st an 2nd degree. Application cases.
Higher polynomial model degree. Application cases.
Polynomial models with consideration relay.
Models of rational functions.
Delaunay Triangulation.
Mixed Models: Theory and examples ASTER, MODIS, SSM/I and SMOS.
Errorestimate.Statistical interpretation of the RMS.
Mosaics and geometry images.
Practical realization of the main models.
RADIOMETRIC IMAGE CORRECTION
1. Radiometric corrections. Calibration sensors. Sources of signal distortion. DN conversion to radiances. Interest and obtaining reflectances.
2. Formulation corrections in the visible and infrared non thermal.
2.1 Sun and atmspheric roles. Exoatmospheric radiance, transmittance. Variation throughout the year. Spectral variation. Diffuse atmospheric radiation.
2.2 Relief role: incidence angle, projected shadows. Celestial sphere. Neighboring reflected radiation.
2.3 Combining sensors in the same study. Usability of pseudoinvariant areas (PIA).
2.4 Combined use of in situ sensors such as handheld spectroradiometers or sun photometers.
3. Corrections based in multispectral and large mount of images: advantages and limitations.
Competences
Apply different methodologies for the primary processing of images obtained by remote sensors in order to subsequently extract geographic information.
Continue the learning process, to a large extent autonomously.
Design and apply a methodology, based on the knowledge acquired, for studying a particular use case.
Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
Take a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.
Use acquired knowledge as a basis for originality in the application of ideas, often in a research context.
Use different specialised GIS and remote sensing software, and other related software.
Learning Outcomes
Continue the learning process, to a large extent autonomously.
Correctly apply methodologies to mitigate the different sources of error in order to visualise and extract physical parameters from the data received.
Design and apply a methodology, based on the knowledge acquired, for studying a particular use case.
Distinguish the different sources of geometric image deformation, and the possible interferences in the captured signal caused by atmospheric effects or illumination effects (topography, etc.).
Show expertise in the physics principles that govern remote image capture and transformations made to the content of the image itself.
Show expertise in using different primary processing tools for aerial and satellite images.
Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
Take a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.
Use acquired knowledge as a basis for originality in the application of ideas, often in a research context.
Presential
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Processing remote sensing Images
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HaDEA. Neither the European Union nor the granting authority can be held responsible for them. The statements made herein do not necessarily have the consent or agreement of the ASTRAIOS Consortium. These represent the opinion and findings of the author(s).
