Geoinformation
Courses
| Earth
Observation
(advanced course)
|
Earth
Observation Data correction
- Geometric correction, registration, resampling
- Radiometric calibration, atmospheric correction
Remote Sensing Data Statistics
Data Transformations
- Principal Components
- Vegetation Indices
- Texture
Thematic information extraction
- Supervised classification
- Unsupervised classification
- Classification accuracy
Neural networks: methods and applications to Earth Observation data
Interferometry: theory and applications to topography, subsidence
and
ice movements monitoring.
Coherency maps. |
Remote
Sensing
(advanced course)
|
Interaction
mechanisms between electromagnetic waves and the environment (reflection,
emission, scattering, propagation)Dependence
of measured electromagnetic quantities on bio-geophysical and meteorological
parameters in the different bands of the electromagnetic spectrumInversion
models and techniquesExtraction
of profiles and maps of bio- and geophysical parameters from
radar, lidar, and radiometric dataThe
use of neural network for classification and parameter estimationSAR
interferometry
- along track, across track, repeat pass
- coregistration
- resampling and interpolation
- flattening
- phase unwrapping
- geocoding
- differential interferometry
- DEM generation |
| |
|
Image
Information Mining
Mihai Datcu
German Aerospace Center DLR
Oberpfaffenhofen, D-82234 Wessling, Germany
&
Paris Institute of Technology GET/Telecom Paris
46 rue Barrault, F-75 013 Paris, France
Starting October 2008 |
The
goal of this course is to promote a new profession that does not
exist today and cannot grow out of the current competencies. The
new profession aims at elaboration of theories, methods and systems
to face in a new manner the new class of problems in image information
extraction, understanding and use.
The new profession is based on a novel class of advanced computer
engineering and information technologies, associated with overall
man - machine system intelligence.
1 Error and quality analysis
2 Information representations
3 Data clustering and grouping
4 Machine learning
4 Spatial syntax and semantics
5 Data Mining: the concepts
6 Knowledge representation and discovery
7 Spatio-temporal reasoning
8 Applications to Earth Observation
References
Image analysis
Parameter estimation
Information measures
Random fields 1
Random fields 2
Image mining 1
Image mining 2
Image time series
KIM
|
Remote
Sensing Instrumentation
William J. Emery
Aerospace Engineering Sciences Department, University of Colorado,
Boulder, CO, U.S.A.
May 2008 |
1.
Overview
(a) Background, Examples of Past, Current, and Future Instruments
(b) Radiative Transfer Basics (specifics of optical and microwave
radiation)
(c) Sensor Systems Engineering: Requirements Analysis and Functional
Design
(d) System Engineering: Design Optimization and Trade Studies
(e) System Engineering: Development, Integration and Test
2. Optical Remote Sensing Instrumentation
(a) Optics Review (Snells law, reflection, refraction, lenses, focal
length)
(b) Optical Design (mirrors, telescopes, optimizing focal lengths,
aperture, field of view, etc., constraints)
(c) Detectors: Overview (photoelectric, semiconductor, CCD)
(d) Detectors: Technological Challenges (real world examples of some
common issues)
(e) Spectral Response (dichroics, filters, hyperspectral approaches)
(f) Instrument Calibration 1
(g) Instrument Calibration 2
(h) Optical Design Example: AVHRR
(i) Optical Design Example: MODIS
3. Passive Microwave Remote Sensing Instrumentation
(a) Introduction to Passive Microwave Remote Sensing
(b) Antennas: Overview (differences and similarities with optical
telescopes)
(c) Antennas: Design (optimization of antenna size, etc.)
(d) Antennas: Technological Challenges (more real world examples)
(e) Synthetic Apertures 1
(f) Synthetic Apertures 2 (example of how to overcome the size constraint
problem)
(g) Instrument Pointing Requirement/control
(h) Design Example: SSM/I , SMOS
4. Active Microwave (Radar) Remote Sensing Instrumentation
(a) Differences Between Passive and Active Microwave Remote Sensing
(b) Radar Design Optimization
(c) Synthetic Aperture Radar
(d) Radar Polarimetry |
Applications
of Neural Networks to Remote Sensing
(2 credits)
The course will be given by Dr. Fabio Del Frate
|
Lecture
# 1: Introduction to Neural Networks
Lecture # 2: Design of optimal networks
Lecture # 3: Applications to image classification
Lecture # 4: Applications to remote sensing of atmosphere
Lecture # 5: Applications to land and vegetation parameters retrieval
Lecture # 6: Towards new neural models and architectures
Laboratory assignment: NEURANUS software |
Modeling
microwave scattering and emission from vegetation: an introduction
The
course will be given by Dr. Leila Guerriero
|
Review
of microwave interaction models for vegetation: from Water Cloud to
coherent models
The permittivity of vegetation
The discrete approach: the canonic representation of the scattering
vegetation element (disc, needle, cylinder); the forward scattering
theorem
Multiple scatterng and combination of contributions by means of the
matrix doubling method: calculation of the backscattering coefficient
and emissivity of vegetated surfaces
Simulations and comparisons with experimental data for wheat, corn,
forests ….
Polarimetry and classification. The bistatic scattering coefficient |
|
