. "Remote Sensing"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Processing remote sensing Images"@en . . "6.00" . "Objectives and Contextualisation\nAt the end of the course, the student will be able to:\n\nMaster different tools primary processing of aerial and satellite imagery.\nDominate the physical principles that govern remote image capture and transformations of the content of the image itself.\nDistinguish the different sources of image geometric deformations and possible signal interference caused by atmospheric captured or lighting effects (topography, etc.).\nCorrectly apply the methodologies to mitigate the different error sources in order to be able to view and extract physical parameters of the received data.\n\nContent\nPHYSICAL PRINCIPLES OF REMOTE SENSING\n\nSolar spectrum\nConcepts: radiation and electromagnetic spectrum, polarization. Fundamental relationships between frequency, length and transported wave energy.\nBasic physical parameters (terminology and symbology, definitions, units): Radiant energy, energy flow, energy intensity, radiance energy excitance, irradiance, reflectance, albedo, transmittance, absorptance; absorbance. spectral magnitudes.\nSpecular reflection, diffuse and lambertiana.\nBlack body (Planck's law, Stefan-Boltzmann law, Wien's displacement law).\nSolar radiation. Exoatmospheric characteristics and the surface of the Earth; interaction with the atmosphere and atmospheric windows.\nSpectral signatures. Main characteristics of water, soil and rocks and vegetation in the visible and infrared non thermal.\nFactors that influence the spectral signature.\nThermal\nThe thermal radiation emitted by the Earth. Remote Sensing approaches.\nPhysical parameters of the thermal infrared region.\nKCL. black body, white body and gray body. selective radiators. Thermal behaviour of an object-related parameters.\nThermal behavior of an object: related parameters.\nSpectral behaviour of the different coverages in the thermal infrared region.\nFactors which influence the emissivity.\nEmissivity measurement. Field measurements.\nEmissivity measurement. Measured from satellite.\nActive microwave\nActive Microwave Remote Sensing: Imaging Radar.\nWave-Matter interaction: Radar Cross Section and Backscattering Coefficient.\nBackscattering Coefficient.\nBackscattering models.\nSAR polarimetry.\nPassive microwave\nPassive Sensors: Fundamentals andPhysical Principles.\nApplications of passive microwave E.O.\nMicrowave Radiometers:\nFigures of Merit: Angular Resolution and Radiometric Resolution.\nCalibration: internal, external, use of multi-look information.\nPresent and future EO Passive Microwave Mission.\nGEOMETRIC CORRECTION OF AERIAL AND SATELLITE IMAGERY\n Geometric corrections. Deformation sources. Orthoimage, orthophoto and orthophoto of authentic orthophotomap concepts. Corrections in vectorial bases.\nPhysical 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.\nGeometry 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.\nBasic correction process. Nearest neighbor, bilinear and bicubic interpolation: Chromatic, radiometric and geometric in image resampling. Considerations about output pixel size.\nSources of GCP. Automatic GCP.\nBasics of physical models. Consideration of the relief.\nBasics of semi-empirical models:\nPolynomial models 1st an 2nd degree. Application cases.\nHigher polynomial model degree. Application cases.\nPolynomial models with consideration relay.\nModels of rational functions.\nDelaunay Triangulation.\nMixed Models: Theory and examples ASTER, MODIS, SSM/I and SMOS.\nErrorestimate.Statistical interpretation of the RMS.\nMosaics and geometry images.\nPractical realization of the main models.\nRADIOMETRIC IMAGE CORRECTION\n\n 1. Radiometric corrections. Calibration sensors. Sources of signal distortion. DN conversion to radiances. Interest and obtaining reflectances.\n 2. Formulation corrections in the visible and infrared non thermal.\n 2.1 Sun and atmspheric roles. Exoatmospheric radiance, transmittance. Variation throughout the year. Spectral variation. Diffuse atmospheric radiation.\n 2.2 Relief role: incidence angle, projected shadows. Celestial sphere. Neighboring reflected radiation.\n 2.3 Combining sensors in the same study. Usability of pseudoinvariant areas (PIA).\n 2.4 Combined use of in situ sensors such as handheld spectroradiometers or sun photometers.\n 3. Corrections based in multispectral and large mount of images: advantages and limitations.\n\nCompetences\nApply different methodologies for the primary processing of images obtained by remote sensors in order to subsequently extract geographic information.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nSolve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nUse different specialised GIS and remote sensing software, and other related software.\nLearning Outcomes\nContinue the learning process, to a large extent autonomously.\nCorrectly apply methodologies to mitigate the different sources of error in order to visualise and extract physical parameters from the data received.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDistinguish the different sources of geometric image deformation, and the possible interferences in the captured signal caused by atmospheric effects or illumination effects (topography, etc.).\nShow expertise in the physics principles that govern remote image capture and transformations made to the content of the image itself.\nShow expertise in using different primary processing tools for aerial and satellite images.\nSolve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context." . . "Presential"@en . "TRUE" . . "Master in Remote Sensing and Geographical Information Systems"@en . . "https://www.uab.cat/web/estudiar/official-master-s-degrees/general-information/remote-sensing-and-geographical-information-systems-1096480962610.html?param1=1345664654736" . "60"^^ . "Presential"@en . "The techniques of remote sensing and Geographic Information Systems (GIS) are essential for studying the Earth and managing its resources, for both academic and business purposes, in such varied fields as the following.\n\nInstruments: satellites, drones, GPS systems.\nGeographical information: cartographic institutes, Bing and Google Maps, OpenStreetMap, etc.\nCataloguing data and metadata in spatial data infrastructures.\nSoftware, such as the free programme MiraMon.\nThis programme has a scientific and technical orientation and gives training in Earth-observation techniques, and in the generation and analysis of information for studying the territory and managing its resources through GIS. It is taught in collaboration with CREAF, GRUMETS, MiraMon and Copernicus Academy."@en . . . . "1"@en . "FALSE" . . "Master"@en . "Thesis" . "5100.00" . "Euro"@en . "6360.00" . "None" . "Specialist in remote sensing and GIS in private companies or public authorities that make wide use of GIS and need personnel with advanced knowledge to manage geospatial data on a daily basis.\nResearcher in both methods and in remote sensing and GIS applications at universities and research centres."@en . "0"^^ . "TRUE" . "Downstream"@en . . . . . . . . .