|Remote sensing and geological imaging|
|30h Th, 10h Pr, 50h Proj.|
Number of credits :
Language(s) of instruction :
Organisation and examination :
|Teaching in the first semester, review in January|
Units courses prerequisite and corequisite :
|Prerequisite or corequisite units are presented within each program|
Course contents :
|This course integrates acquisition, processing and analysis of images in geosciences with application in the lab, in industrial vision and in remote sensing|
Learning outcomes of the course :
|To give students a full overview of image processing and analysis in the geosciences
To familiarize students with the main techniques for image acquisition and image processing with a particular emphasis on mathematical morphology
To give students the possibility to practice digital imaging and develop their critical perception of applications in geology
To provide guidelines for selecting appropriate hardware and software tools to solve a given problem.
Prerequisite knowledge and skills :
Planned learning activities and teaching methods :
|1. Introduction to Image Analysis and Stereology
Induction vs deduction
The importance of vision in geosciences
Stereology and applied mineralogy
2. From minerals to pixels : basic principles of imaging
What is an image?
2D scanning geometry for imaging
3D surface imaging
3D volume imaging
Scientific imaging in microscopy
From analog to digital images
Digital image file formats
3. Physics of Remote Sensing.
Radiance and reflectance.
Sources of electromagnetic radiation
Atmospheric corrections, calibration methods
Spectral properties of minerals, rocks and soils VNIR and SWIR ranges
Multispectral, superspectral and hyperspectral remote sensing
4. Technology of Earth Observation : platforms and sensors.
Spatial characteristics of RS data.
Spectral characteristics of RS data.
Examples: Landsat TM and ETM+, SPOT 3-4-5, ASTER, IKONOS, QuickBird, CASI, AVIRIS, HyMap.
5. Image Processing
Image processing operations (global vs. local)
Linear filters (low-pass, hi-pass, gradients)
Mathematical Morphology (erosion, dilation, opening, closing)
Spectral classification tools (thresholding, µgaussian, ...)
Geodesic operators and distance functions
Spatial segmentation (labelling, hole-fill, watershed, SKIZ,...)
Introduction to mixed (spectro-spatial) segmentation
6. Image analysis and processing, optical VNIR SWIR and thermal domains.
Geometric corrections, georeferencing.
Radiometric correction, calibrations, atmospheric corrections
Multispectral data processing.
Data fusion, image sharpening
Band ratios, indexes
Data transforms: principal components analysis, Munssell HIS.
7. Quantitative mineralogical and textural analysis
Modal (phase) and porosity analysis
Blob analysis: particle size and shape analysis
Network analysis: Characterizing size distributions in a continuous phase.
Quantitative microstructural and textural analysis: characterizing spatial arrangement
Mode of delivery (face-to-face ; distance-learning) :
2 hours theory + 2 hours practice
Recommended or required readings :
|PIRARD E., SARDINI P., Image analysis for advanced characterization of geomaterials, EMU Lecture Notes, 2009
PIRARD, E., 2004, Chapter IV. Image measurements
in P. FRANCUS (Ed) "Image analysis, sediments and paleoenvironmental reconstruction", Kluwer, NY
PIRARD, E. et CACERES, F., 2004, Télédétection et télégestion des informations géologiques : de nouvelles technologies au service du développement. L'exemple du Sud Lipez (Bolivie).
Assessment methods and criteria :
|Oral examination (duration 1h)
30' written preparation + 30' oral presentation
Work placement(s) :
Organizational remarks :
|Mlle Nadia ELGARA
Secrétariat Dpt GeMMe