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Grenoble, 2008/09/08
VHR Systems Missions Challenges Development Image Processing New missions
The example of disaster management
Grenoble, 2008/09/08
VHR Systems Missions Challenges Development Image Processing New missions
The example of disaster management
Grenoble, 2008/09/08
VHR Systems Missions Challenges Development Image Processing New missions
The example of disaster management
Grenoble, 2008/09/08
VHR Systems Missions Challenges Development Image Processing New missions
New constraints
I Short system time responseI Much more than a short revisit cycleI Reactive satellite taskingI Quick image availability
I Actually, information availabilityI High spatial resolution + wide swath
I Being able to see details ...I ... without actually knowing where to look for them!
I High spatial + spectral resolutionI Geometric and radiometric information to finely discriminate
objects and their characteristicsI Permanent (geostationary) and high resolution (low orbit)
observation
Grenoble, 2008/09/08
VHR Systems Missions Challenges Development Image Processing New missions
How to find a solution
I Don’t worry, space agencies have clever people to solvethis kind of problems
I Inter-satellite links for retasking and up/downlinksI Agile platforms for one-pass image mosaicsI Virtual huge telescopes by aperture synthesisI Deployable antennasI Constellations of smaller, cheaper satellitesI Many clever devices that you thought couldn’t exist out of
Star TrekI And other top secret things I can’t talk about ...
Grenoble, 2008/09/08
VHR Systems Missions Challenges
Still any challenge for IP people?
I The solutions cited above are still expensiveI An IP-based solution is softwareI Software is cheap. Best software is free!
I Users’ expectations go further and further (and quicker)I Everybody has a GPS receiver, Google Earth, Open Street
Maps, etc.I Thus new imaginative uses for geospatial information
appear every dayI Updating software is quicker and cheaper than building a
satellite
Grenoble, 2008/09/08
VHR Systems Missions Challenges
New value for old sensorsI RS scientists are good at finding new uses for existing
sensorsI some examples were given beforeI sometimes is just a proof of concept: DEM extraction with
SPOT 5 P+XSI sometimes operational applications are built upon these
ideas: Persistent Scatterers InterferometryI In some sense, the International Charter Space and Major
Disasters uses this approachI Most of the available systems are not designed for disaster
managementI 2 paths leading to these new ideas
I a real need triggers the idea:I how can I deliver this fancy information with this set of old
crappy sensors?I take the system to its limits: squeeze the resolution,
interpolate, deconvolve
Grenoble, 2008/09/08
VHR Systems Missions Challenges
Existing ancillary data
I Why extracting from images what you already (nearly)have elsewhere?
I Maps, vector data bases, the Web, etc.I Remember Kalman
I Update your guesses (ancillary) with new data (RS)I However
I How to assess the quality of ancillary data?I How to compare ancillary data and RS images?
I Level of representation?I How to assess the quality of the updated geoinformation?
I Measuring the performances of the algorithms: reliability,precision, accuracy, etc.
Grenoble, 2008/09/08
VHR Systems Missions Challenges
Measuring the performances of a system
I AssessI the quality of the input informationI the performances of each individual processing blockI the quality improvement introduced by fusion
I So the quality of the output is known
Grenoble, 2008/09/08
VHR Systems Missions Challenges
Fusing new kinds of data
I Beware of fancy, useless fusionI Have you heard of optical/SAR fusion? Me too.I Have you seen real applications of it? Me neither!
I The way to put it is:I Given a user need, which is the most straightforward way
(data availability, price, delays) to get the information?I If this means that fusion is needed, go for it.I Usually, pixel-level fusion is not the best way to do it
I except for pan-sharpening, and maybe medium resolutionmulti-temporal series
I On the other hand:I Fusing any kind of data may be possible. Just choose the
appropriate representation level.
Grenoble, 2008/09/08
VHR Systems Missions Challenges
New computing approaches
I VHR means huge data volumeI Parallelism, streaming:
I These are techniques allowing to overcome computationtime and memory capability limitations
I They need to split the data in order to process themI Algorithms which can not process data by chunks are dead!
I Scalability of algorithms is crucial for end-user applications