Remote Sensing

Moving the blog

2011-04-16T13:28:00.001-07:00

I am in the phase of moving my blogs to

http://blog.jordiinglada.net/

2011-04-01T08:47:00.001-07:00

PROBA-V Preparatory Programme - PRO-Fusion

PROBA-V stands for PRoject for On-Board Autonomy - VEGETATION and is a space mission aimed at ensuring the data continuity between the Végétation missions (on board of CNES' SPOT-4 and SPOT-5) and ESA's Sentinel-3.

PROBA-V has a Preparatory Programme funded by Belspo, the Belgian Federal Scientific Policy Office. I am PI for one of the selected projects of this programme. As such, I participated at the end of February to one of the International Users Committee to present the goals of the project.

The project is called PRO-Fusion and it aims at developing and assessing algorithms for the fusion of PROBA-V images with other types of images as for instance Venus or Sentinel-2.

The slides of my presentation are available here.

One interesting thing of this project is that the source code of the algorithms will be available as open source software. I have already started to contribute some code to the Orfeo Toolbox. The main contributions involve the multi-temporal processing.

2011-02-26T12:45:00.001-08:00

HDF4 support for GDAL on Arch Linux

I have been trouble reading HDF files with OTB on Arch Linux for a while. I finally took the time to investigate this problem and come to a solution.
At the beginning I was misled by the fact that I was able to open a HDF5 file with Monteverdi on Arch. But I finally understood that the GDAL pre-compiled package for Arch was only missing HDF4 support.
It is due to the fact that HDF4 depends on libjpeg version 6, and is incompatible with the standard current version of libjpeg on Arch.
So the solution is to install libjpeg6 and HDF4 from the AUR and then regenerate the gdal package, who, during the configuration phase will automatically add HDF4 support.
Here are the detailed steps I took:

Install libjpeg6 from AUR:
1. mkdir ~/local/packages
2. cd ~/local/packages
3. wget http://aur.archlinux.org/packages/libjpeg6/libjpeg6.tar.gz
4. tar xzf libjpeg6.tar.gz
5. cd libjpeg6
6. makepkg -i #will prompt for root psswd for installation
Install HDF4 from AUR:
1. cd ~/local/packages
2. wget http://aur.archlinux.org/packages/hdf4-nonetcdf/hdf4-nonetcdf.tar.gz
3. tar xzf hdf4-nonetcdf.tar.gz
4. cd hdf4-nonetcdf
5. makepkg -i #will prompt for root psswd for installation
Setup an Arch Build System build tree
1. sudo pacman -S abs
2. sudo abs
3. mkdir ~/local/abs
Compile gdal
1. cp -r /var/abs/community/gdal ~/local/abs
2. makepkg -s #generated the package without installing it
3. sudo makepkg -U gdal-1.8.0-2-x86₆₄.pkg.tar.gz

If you are new to using AUR and makepkg, please note that the AUR package installation need the sudo package to be installed first (the packages are build as a non root user and sudo is called by makepkg).
Step 3 above is only needed if you have never set up the Arch Build System on your system.

2011-02-09T03:26:00.001-08:00

Understanding map projections

Map projections are both easy and tricky. I am not a specialist at all about the subject, but I have used them a little bit.

When I say that map projections are easy, I mean that, even without understanding them completely, there are tools which allow a user to get the work one. Of course, I use the Orfeo Toolbox library, which in turn uses the OSSIM library. Actually, some years ago, with a student, we designed the interface in order to integrate OSSIM map projections (and sensor models too) into OTB so that they could be used as the existing geometric transforms already available in OTB (which came from ITK).

The only thing we had to understand in order to design this interface was that map projections are coordinate transformations. Then, we chose the appropriate object-oriented design pattern together with a bit of C++ templates for the generic programming ingredient and that was it.

When some users started using the projections (well, WE were those users) questions arised about the projections themselves. The task in answering to these questions would have been much easier if we had had this info (posted by Caitlin Dempsey on GIS Lounge):

/"The USGS has posted a scanned file of John P. Snyder's 1987 "Map/ Projections: A Working Manual" online in PDF and DjVu format. The beginning of the book contains substantial introductory information about map projection families and distortions. Each projection is started with a useful summary of the context and usage of that particular projection. Snyder then delves into detail about the history, features, and usage before providing the mathematical /formulas used to calculate the projection."/

2011-02-06T07:38:00.001-08:00

Traffic Monitoring with TerraSAR-X

I have just read this interesting article which describes away to measure vehicle speeds using space-borne SAR sensors. The article explains very clearly how Doppler effect can be used, either with one single image, or with an interferometric pair, to estimate the speed of cars or ships.

DLR's TerraSAR-X system has always impressed me. It has been providing very high quality images from its launch. For having used the images, I can say that their geometrical quality is very good. And as far as I know, this is the only operational system in flight which is able to perform one-pass interferometry.

So far so good.

However, the article forgets to mention that these satellites acquire only 2 images per day on a given point of the Earth's surface -- typically in the morning and in the evening – so one can not expect to use this technology for real-time traffic monitoring.

So you don't need to worry about getting a ticket for speed.

Author: Jordi Inglada

Teenager contribution for remote sensig

2011-01-30T04:43:00.000-08:00

Check out this article. A teenager proposes an instrument concept for measuring the temperature at the bottom of clouds. NOAA is testing it.

Multi-temporal series simulations

2011-01-24T05:41:00.000-08:00

As I mentioned in a previous post, last September I participated to the Recent Advances in Quantitative Remotes Sensing Symposium. I presented several posters there. One of them was about the assessment of the classification accuracy of Venus and Sentinel-2 sensors for land cover map production.

While the results of the study are interesting, I think that the mostimportant thing that this paper shows is how a time series withrealistic reflectance values can be simulated.

The idea here is to find a good balance between image synthesis (low accuracy) and physically sound simulation (need for ancillary data and computational complexity). The choice made here is to use a real time series of Formosat-2 images (only 4 spectral bands) in order to simulated Venus and Sentinel-2 time series with the same temporal sampling but with more than 10 spectral bands.

The Formosat-2 time series is used in order to:

Estimate the LAI (leaf area index) for each pixel
Give a spatial distribution using a land-cover map

A database containing leaf pigment values for different types of vegetation is used together with the above-mentioned LAI estimation in order to drive a reflectance simulator. The simulated reflectances are then convolved with the relative spectral responses of the sensors in order to generate the simulated images.

The poster presented at RAQRS 2010 is here.

Reproducible research

2011-01-03T01:50:00.000-08:00

I have recently implemented the Spectral Rule Based Landsat TM image classifier described in:

Baraldi et al. 2006, "Automatic Spectral Rule-Based PreliminaryMapping of Calibrated Landsat TM and ETM+ Images", IEEE Trans. on Geoscience and Remote Sensing, vol 44, no 9.

This paper proposes a set of radiometric combinations, thresholds and logic rules to distinguish more than 40 spectral categories on Landsat images. My implementation is available in the development version of the Orfeo Toolbox and should be included in the next release:

One interesting aspect of the paper is that all the information needed for the implementation of the method is given: every single value for thresholds, indexes, etc. is written down in the paper. This was really useful for me, since I was able to code the whole system without getting stuck on unclear things or hidden parameters.

This is so rarely found in image processing literature that I thought it was worth to post about it. But this is not all.

Once my implementation was done, I was very happy to get some Landsat classifications, but I was not able to decide whether the results were correct or not. Since the author of the paper seemed to want his system to be used and gave all details for the implementation, I thought I would ask him for help for the validation. So I sent an e-mail to A. Baraldi (whom I had already met before) and asked for some validation data (input and output images generated by his own implementation).

I got something better than only images. He was kind enough to send me the source code of the very same version of the software which was used for the paper – the system continues to be enhanced and the current version seems to be far better than the one published.

So now I have all what is needed for reproducible research:

A clear description of the procedure with all the details needed
for the implementation.
Data in order to run the experiments.
The source code so that errors can be found and corrected.

I want to publicly thank A. Baraldi for his kindness and I hope that this way of doing science will continue to grow.

If you want to know more about reproducible research, check this site.

Change detection of soil states

2010-11-23T08:28:00.000-08:00

As I mentioned in a previous post, last September I participated to
the Recent Advances in Quantitative Remotes Sensing Symposium. I
presented several posters there. One of them was about the work done
by Benoît Beguet for his master thesis while he was at CESBIO earlier
this year.

The goal of the work was to assess the potential of high temporal and
spatial resolution multispectral images for the monitoring of soil
states related to agricultural practices.

This is an interesting topic for several reasons, the main ones being:

a bare soil map at any given date is useful for erosion forecast
and nitrate pollution estimations

the knowledge about the dates of different types of agricultural
soil work can give clues about the type of crop which is going to
be grown

The problem was difficult, since we used 8 m. resolution images (so no

useful texture signature is present) and we only had 4 spectral bands

(blue, green, red and near infrared). Without short-wave infra-red

information, it is very difficult to infer something about the early and late

vegetation phases.

However, we obtained interesting results for some states and, most of
all, for some transitions – changes – between states.

You can have a look at the poster we presented at RAQRS here

Change detection of soil states

View more documents from inglada.

Massive Remote Sensing

2010-10-31T10:26:00.000-07:00

Some weeks ago I had the chance to assist to the 3r Symposium on
Recent Advances in Quantitative Remote Sensing, RAQRS, in València,
Spain.

It was the first time I attended such a conference. I've been to
nearly all IGARSS since 1998, but I had never been to a conference
where the main topic was the physics of remote sensing of continental
surfaces. All in all, it was a very interesting and inspiring
experience and I learned a lot of things.

Many of the talks and posters dealt with applications related to
multi-temporal and metric to decametric resolutions. This is due to
the fact that most of the phenomena of interest (as for instance, the
Essential Climate Variables, ECVs for short) are best monitored at
those resolutions.

It was in this context that I heard the massive remote sensing
expression. As I understand it, it makes reference to the future
available flow of data produced mainly by the ESA's Sentinel
missions. Indeed, with these sensors (and others as NASA's LDCM) a
frequent complete cover of the Earth's surface by high resolution
sensors will be available. And, in order for these data to be useful,
fast and efficient automatic processing methods will be needed.

This last sentence may seem as nothing new with respect to what has
been said inn recent years about very high spatial resolution sensors,
but I think that now there are several issues which make it really
crucial:

Always on: the Sentinels (at least 1 and 2) will always be
acquiring data, so the amount of images will be huge.

Data really available: I don't know if this has been officially
validated by ESA, but, as far as I know, the images will be free of
charge or at a minimum cost

Physical reality: the sensors will not be just taking pictures,
but provide many spectral bands which can not be easily visually
analyzed.

So I think it's time to start taking this challenge seriously and
addressing the tough points such as:

How to produce global land-cover maps without (or with very little)
ground truth?

How to develop models and methods which can be ported from one
site to another with minimal tuning?

How to exploit the synergy between image data and ancillary data or
between image modalities (Sentinel-1 and Sentinel-2, for instance).

Sweet Remote Sensing

2010-09-30T03:08:00.000-07:00

For a few months now, I have been taking a look at software tools designed for kids' education. There are many of them available, but I have been focusing on the works of the community founded by S. Papert and Alan Kay, inventors of the Logo and Smalltalk programming languages respectively. Papert and Kay got inspiration from the pioneer figures of pedagogy Piaget and Montessori and they took constructivism as main leading path for their developments.

The main idea behind constructivism is that the kid (but also adults as a matter of fact) learn by doing and exploring concepts. Papert and Kay adapted this theory to the use of computers: computers are simulators of the physical reality and as such, they allow to learn by doing even for concepts for which a physical world experience would not be feasible. The Squeak eToys environment is a good example of this.

One thing that struck me is that these ideas lead to (among others) the following conclusion: give a kid a laptop loaded with open source software and an Internet and she can learn whatever she wants. This is not just theory. The best example of the application of this idea is the OLPC (One Laptop Per Child) project and its success.

The OLPC software is based upon a software suite called Sugar which is made of lots of so-called activities for different disciplines: math, languages, physics, etc. This open source environment allows a kid to learn things, not just by using the activities, but also by allowing her to look at the source code (how things are done).

The strength of this kind of environment is that it can be used at several levels which allow the user to use the optimal trade-off between power and easy of use.

I would like to see an analogous environment for Remote Sensing Image Processing and Analysis. The Orfeo Toolbox is of course a very good candidate for this, and many steps have been taken in this direction.

The work of architecture and integration of the best existing open source libraries (ITK, OSSIM, 6S, libSVM, etc.) has provided the kitchen sink, the power. The creation of bindings (python, java), has allowed to expand the ways in which this power can be accessed. Finally, the GUI-based application Monteverdi has made the point and click use of (a limited part of) this power possible. The availability of QGIS plugins demonstrates that other paths can be imagined to make OTB usable by non programmers.

However, there is still lots of work to be done in order to have a completely integrated environment where, from the same starting point, a user can navigate through the different levels of complexity and power. Even if this is not an easy goal achieve, I think that all the building blocks are available to progress in this direction. This may only be a (very) long term goal, but I think that the existence of this vision can help to federate a community and motivate people to contribute.

Community building

2010-07-01T00:58:00.000-07:00

In my last post I wrote about open approaches for Earth Observation. Open (science/source/whatever) means community. I am in the middle of the reading of "The Art of Community" and it is very inspiring. In some ways, it reminds me of very much of Chapter 4 "Social and Political Infrastructure" of Karl Fogel's "Producing Open Source Software - How to Run a Successful Free Software Project", although Bacon's book is more general.

Anyway, both books give very good insight on the issues and tricks involved in a community-based project and even the particular case of projects which are created and managed by companies.

This is an interesting point, since one could think that a project funded by a company has more chances to succeed than a pure volunteer-based one, but many real examples show that this is not the case. The main pitfalls of a corporate open source project are related to decision making and communication. For a project to succeed, the community has to be respected and open discussions and meritocracy are the 2 pillars of community.

Examples of this are the fact that all decisions involving the development have to be explained and discussed in a way that all developers are involved. Examples of closed discussions at the company level leaving out the volunteer contributors are given.

Another interesting example is the one given by Fogel about meritocracy: any developer should earn the write access to the source repository by proving his value. Usually, in corporate environments, developers have commit access from the first day, while external contributors, which usually are much more capable, have to wait long time before that.

An important player in any open source project is the Benevolent Dictator (BD). I will cite Fogel verbatim here:

"It is common for the benevolent dictator to be a founder of the project, but this is more a correlation than a cause. The sorts of qualities that make one able to successfully start a project—technical competence, ability to persuade other people to join, etc.—are exactly the qualities any BD would need. And of course, founders start out with a sort of automatic seniority, which can often be enough to make benevolent dictatorship appear the path of least resistance for all concerned.

Remember that the potential to fork goes both ways. A BD can fork a project just as easily as anyone else, and some have occasionally done so, when they felt that the direction they wanted to take the project was different from where the majority of other developers wanted to go. Because of forkability, it does not matter whether the benevolent dictator has root (system administrator privileges) on the project's main servers or not. People sometimes talk of server control as though it were the ultimate source of power in a project, but in fact it is irrelevant. The ability to add or remove people's commit passwords on one particular server affects only the copy of the project that resides on that server. Prolonged abuse of that power, whether by the BD or someone else, would simply lead to development moving to a different server.

Whether your project should have a benevolent dictator, or would run better with some less centralized system, largely depends on who is available to fill the role. As a general rule, if it's simply obvious to everyone who should be the BD, then that's the way to go. But if no candidate for BD is immediately obvious, then the project should probably use a decentralized decision-making process ..."

And I will end this post by using the opening quote of Bacon's chapter 9:

"The people to fear are not those who disagree with you, but

those who disagree with you and are too cowardly to let you

know."

—Napoléon Bonaparte

Food for thought.

Open Remote Sensing

2010-06-19T08:14:00.000-07:00

Right after the recent Haiti earthquake a community of volunteers put their efforts together in order to build up to date maps of the area in order to help the humanitarian aid on the terrain.

This community is the one who is contributing to Open Street Map (OSM), the wiki-like world wide map on the Internet. A detailed presentation about this activity can be watched here.

This kind of activity can only be carried out if the volunteers have the input data (images, gps tracks) and the appropriate tools (software).

In terms of software, the OSM people rely on open source state of the art tools which allow them to be quick and efficient. As far as I know, their infrastructure is based on OSGEO tools for the storage, the formats, the web services for edition and visualization, and so on.

So open people with open tools generating open maps is something which has been proven useful and efficient for a while now: just take a look at the status of OSM near where you live in order to convince yourself. This is another example of the Wisdom of Crowds phenomenon.

However, one thing which was really special in the Haiti case is the speed at which the maps where created after the earthquake. This may be surprising when one thinks that most of the work for OSM is based on gps tracks submitted by people which are on the terrain or by volunteers digitizing somewhat outdated aerial imagery.

In the Haiti case, what really allowed for a quick cartographic response was that space agencies and satellite image providers made freely available the images acquired not long before and after the event. This allowed for really accurate maps of the communication infrastructures and buildings as well as a damage assessment of those. Refugee camps could also be easily located.

Unfortunately, the availability of this kind of data is not usual. Even with initiatives as the Disasters Charter the images made available by the signing parties and the added value maps are distributed under licenses which are relatively restrictive.

And as one can easily understand, the open people using open tools to create open maps are completely useless if they don't have the images to work with. Many people will agree on the fact that this should change when lives are at stake. Other people will think that, even for general purpose cartography, data should be freely available. Of course, space agencies, the aerospace industries, etc. have hard (economical and industrial) constraints which don't allow them to give away expensive images.

And of course, a bunch of volunteers don't have the resources to put satellites in orbit and acquire images. One thing is writing free software and another thing is rocket science! Or is it?

It seems that a group of people are thinking about doing things not far from that.

The Open Aerial Map project aims at building a world wide cover with free aerial images. Even if the project has been dead for a while, it seems to be active again.

Another interesting initiative is the Paparazzi project which aims at building open source software and hardware for aerial unmaned vehicles. One of the projects using it is made by the University of Stuttgart and aims at developing a system for high resolution aerial imagery.

The list of interesting free projects which could be used to setup an open source global nearly real time mapping system is long. It is likely that all the bricks will be put together someday by volunteers.

If this comes to reality, I suggest just one restriction on the license: the user wanting to print the map will have to read Borges' "Universal History of Infamy" before choosing the scale.

Short revisit cycle and temporal sampling

2010-05-20T13:40:00.000-07:00

There is a somewhat recent trend in the development of new remote sensing satellite systems toward short revisit cycle (that is a high temporal resolution). Future systems such as Venµs or the Sentinels will be providing images of a given site every 2 to 5 days.

One may wonder which real applications may need this high temporal resolution. Although there are some applications which need it, it is also true that most of the applications foreseen for these systems will work with fewer images per period of time.

However, there are several point to take into account when we think about temporal sampling. First of all, at least for optical systems, the cloud cover can make unusable a high percentage of the images which are acquired by the sensors. Second, even if the mean number of images per year for a given application may be low, some of the applications may need a higher temporal resolution during some periods of the year (example of the phenology of crops). And the fact is that flight mechanics make easier to keep the same orbit all over the life span of a satellite.

And last, but not least, many new applications can emerge when this kind of data is made available to scientists.

In the coming versions of the Orfeo Toolbox, specific tools for image time series will be made available in order to help the users of this kind of data to use them efficiently.

Open tools for modeling and simulation

2010-04-27T11:59:00.002-07:00

I has been now nearly 2 months since I moved to a new job. My research subject has slightly evolved, from sub-meter resolution image information extraction (mainly object recognition for scene interpretation) towards high temporal resolution for environment applications. The change in the resolution dimension (from spatial to temporal) is the main re-orientation of the work, but if I have to be honest, this would not be challenging enough to justify leaving what I was doing before, the nice colleagues I had (though the new ones are also very nice), etc.

The main challenge of the new job is to dive in the world of physics and modelling. Although I did a lot of physical modelling and simulation in my PhD, this was 10 years ago and it was SAR over the ocean, while now it is mainly optical over continental surfaces.

I have the chance to have landed on a lab with people who are specialists of these topics, so I can ask lots of questions about very specific issues. The problem is that I am lacking basic knowledge about state of the art approaches and I don't like to bother my new colleagues (since they are nice!) with stupid questions.

There is where open available ressources are very helpful. I will just cite 2 pointers among the lots of relevant stuff I am using for my learning.

In terms of modelling the physical and biological processes in an agricultural field, I found Daisy, which is an open source simulation system developed by Søren Hansen's team at the University of Copenhagen. Added to the source code, there is a very rich theoretical documentation available.

Once these processes are understood and simulated, I was also interested in learning how things can be measured by optical sensors. I found Stephane Jacquemoud's PROSAIL approach for which source code and reference documentation (papers, PhD dissertations) are available online.

From there, I just put things together in order to learn with a hands on approach. PROSAIL is Fortran and Daisy is C++. I wanted to plot some simulated vegetation spectra. So I fired up Emacs and started writing some python code in order to loop over variable ranges, launch the simulators, plot things with Gnuplot.py, and so on. And then, I remembered that we have python wrappers for OTB, which would make possible the use of the 6S radiative transfer code using OTB's internal version of it.

And here we are, with a fully open source system which allows to do pysics-based remote sensing. Isn't that cool?

2010-04-27T11:59:00.001-07:00

There is where open available ressources are very helpful. I will just cite 2 pointers among the lots of relevant stuff I am using for my learning.

And here we are, with a fully open source system which allows to do pysics-based remote sensing. Isn't that cool?

Geospatial analysis and Object-Based image analysis

2010-03-20T07:21:00.000-07:00

I was searching in the web about the use of PostGIS data bases for object based image analysis (OBIA) and Google sent me to the OTB Wiki to a page that I wrote 6 months ago (first hit for "postgis object based image analysis").

It seems that this is a subject for which no much work is ongoing. Julien Michel will be presenting some results about how to put together OBIA and geospatial analysis (+ active learning and some other cool things) in a workshop in Paris next May.

Changing places

2010-02-19T05:07:00.000-08:00

Today is my last day at my current position. On the 1st of March I will be at my new position at CESBIO.

This is not a major change. I keep working for CNES in remote sensing image processing, but I will be more focussed on multi-temporal image series (preparing Venµs and Sentinel-2 data use). The applicative context of all this work will be the Land Use and Cover Change.

I am mainly interested in introducing physical prior knowledge in image analysis techniques.

Simulation in Remote Sensing

2010-01-20T03:22:00.000-08:00

Remote sensing images are expensive to buy. Remote sensing sensors are very, very expensive to design and build. Therefore, it may be interesting to know, before investing any money in images or sensors, which are the capabilities of an existing or future sensor.

In the Spring of 2009, Germain Forestier was a visiting scientist at CNES and we worked on this subject.

We (well, it was actually him who did the work!) implemented a simple simulator which used several spectral data bases, a set of sensors' spectral responses and generated as output the spectra which would have been obtained for each material of the database by each of the sensors.

The, we just applied classification algorithms in order to assess the quality of the classification results for each sensor. This simulator did not integrate atmospheric effects or spatial resolution information, so the conclusions drawn can not be used as general truth. However, we could show interesting things such as for instance, that the better results obtained by Pleiades HR with respect to Quickbird are due to the different design of the Near Infra-Red band (full disclosure: I work at CNES, where Pleiades was designed).

The detailed results of this work were published last Summer: G. Forestier, J. Inglada, C. Wemmert, P. Gancarski, Mining spectral libraries to study sensors' discrimination ability, SPIE Europe Remote Sensing, Vol. 7478, 9 pages, Berlin, Germany, September 2009

After Germain's leave at CNES, we have continued to work a little bit on the simulator in order to make it more realistic. We have already included atmospheric effects and plan to go further by introducing spatial resolution simulation.

I am convinced that this is the way to go in order to cheaply assess the characteristics of a given sensor in terms of end-user needs and not only in temrs of system quality issues as for instance SNR.

Multi-temporal analysis at IGARSS 2010

2009-12-21T02:29:00.000-08:00

Professor Lorenzo Bruzzone has been extremely kind with me by proposing to me the co-chair
position for an invited session at IGARSS 2010. The session is entitled "Change Detection ad Multitemporal Image Analysis".

Land use and cover change is a major topic in remote sensing image processing and, as you may be aware of, there are many coming space borne systems which will be dedicated to this kind of application: Venus, the Sentinel Programme, etc.

In this context, there is an increasing need for processing techniques which allow to exploit the richness of this kind of data. The classical approach for the use of multi-temporal remote sensing
image data has been the use data assimilation frameworks. That is, models of evolution in which data is used mainly as a means for checking the soundness of models' predictions. This approach gives more weight to the model, since the data is scarce.

As pointed out above, in the coming years the amount of available data will be important, and one might want to try other approaches where data takes over models. Of course, it would be wrong to blindly apply data analysis tools without using existing models, but the assimilation techniques may benefit from changes induced by the availability of more data.

Wikinomics

2009-09-12T04:17:00.000-07:00

How mass collaboration changes everything

I read this very interesting book and I truly recommend it.

Just one thought: what about remote sensing?

Google LatLong: 3 Days, 3 Googlers, 2 CPUs, 8 Cores: Google goes to Camp Roberts

2009-08-26T00:06:00.000-07:00

A very interesting post about how geospatial analysis software can be useful for humanitarian tasks:

Google LatLong: 3 Days, 3 Googlers, 2 CPUs, 8 Cores: Google goes to Camp Roberts

Surely, OTB could be useful for this kind of application. It has already been used in real cases as for instance the International Charter Space and Major Disasters.

Changing the processing paradigm?

2009-07-04T07:27:00.000-07:00

No I am not going to write about compressive sensing. I am sorry.

The paradigm I want to write about is the one currently used for mapping applications from remote sensing imagery. This way of turning images into maps can be resumed as ortho-analysis-GIS, that is, you get the image and convert it into a map projection, then you analyze it (segmentation, classification, etc.) in order to produce vector layers and finally you import these vector layers into a GIS in order to perform geospatial analysis (eventually fusing with existing vector maps) and you produce the final map.

This works OK, but is not the most efficient way of working. If you look at the final map, how much information really came from the images? How may pixels were really useful for producing this information? The answer is usually "not much".

Now look at the computation time needed by all the processing steps in the map production chain. Can you guess what is the most expensive step? With current high resolution images, the ortho-rectification step is the most time consuming.

One solution to this could be to ortho-rectify only the interesting area for the application. The drawback of this approach is that usually, you need to process the image (detect the pertinent features, changes, etc.) before you know where is the interesting information.

In this case, the solution, would be to process the image before ortho-rectification. There is one main problem to this : many modern software tools for segmentation and classification are not very good at processing huge images. Even if they were good at it, you would need to process the whole image before ortho-rectification.

The thing is that often, the existing maps tell you where the interesting things are likely to be found, but since your maps are "ortho", you still need to ortho-rectify your image before processing.

Also, the geospatial reasoning step is made at the end of the processing, inside the GIS tool, which usually knows very little about image processing and so.

So it seems that the paradigm cited above (which could also be named ERDAS-Definiens-ArcGIS, for example), although useful has real drawbacks for efficiency. And I am not talking about import/export and format issues.

In order to be really efficient, we would need a tool which would allow us to send the existing shapefile or KML maps on top of the image in sensor geometry, perform some geospatial reasoning up there, segment and classify only the areas of interest (still in sensor geometry), produce vector data and finally send only the useful information down to the map projection.

Hey, I finally nearly wrote about compressive sensing, didn't I?

To finnish: don't tell anybody, but it seems there is a free software out there which is able to do what I have just wrote. Well the PostGIS interface is not yet ready, but it is on its way.

Can't use an atlas for geoinformation

2009-04-27T04:31:00.000-07:00

Wikipedia defines an atlas as a collection of maps. Atlases were created to represent geographically distributed information on the Earth surface: physical geography, political geography, and any socioeconomics information for which the geographic distribution makes sense.

Atlases are also used in anatomy. They describe how the animals' organs are located into the bodies. The term atlas here is used by analogy to the geographical ones.

Anatomy atlases are often used in medical image processing, since they allow to introduce prior knowledge about the object of study in a convenient way. This way, spatial ontologies can be built to guide segmentation or classification algorithms. The relationships between different objects in the body are known and described in the atlas.

In the same way as anatomy (and as a consequence medical image processing) borrowed the term atlas from geographic information, remote sensing scientists have tried to get inspiration from medical imagery in the how prior information can be used for remote sensing scene analysis and interpretation.

In this context, atlases or ontologies have been developed in order to represent how the objects present in a remote sensing image are distributed and what are their mutual relationships.

Not surprisingly, there has not been a major break-trhough in remote sensing image analysis using this kind of technique.

In my opinion, this is due to the fact that the object of the study can contain much more variability in remote sensing than in medical imaging. For example, the spatial relationships between different parts of the brain are much less subject to variability than, for instance, the relationships between the buildings which make up an airport.

However, nearly any human operator, even not very much trained, can recognize an airport in a remote sensing image. Why?

Well, I guess that in anatomy, the spatial relationships are some kind of optimal solution to a problem which is posed in terms of functional relationship and species evolution. This is closely linked to morphogenesis.

The organization of geographic entities linked to anthropic activities is also the result of a solution of a problem posed in terms of functional relationship: the buildings of an airport must comply with a certain number of functional needs (access to planes, security regulations, etc.), but they are only a solution, not the optimal one. After all, these are solutions proposed by men, not by nature after several millions of years of evolution!

The Wiki Power

2009-02-13T07:38:00.000-08:00

Last week I could experiment the power of cooperative work on the Internet.

In the frame of the development of the ORFEO Toolbox (OTB), I was doing some bibliographic research. I wanted to find as many radiometric indices as possible that I could compute using optical multispectral remote sensing data. My problem was mainly focussed on Pleiades-like data, but I thought that it would be interesting to widen the search. After all, these indices are going to be coded in OTB, which has a rather heterogeneous user base.

Unfortunately, I have no easy access to application oriented remote sensing journals, so I was facing a tedious work.

I started with the most well known indices as NDVI and I wrote some descriptions for them in the OTB Wiki. As I was on the wiki, I thought I could ask some other people to complete the documentation, so I sent an e-mail to several mailing lists where I knew that people knew about radiometry issues.

To be honest, at the beginning I thought that it would be useless. I had a good surprise, when the next morning I saw that somebody had created a new account on the wiki and was formatting and enriching (adding some descriptions and bibliographic references) to the list of indices I had started. The day after that 2 other people joined the effort and added new indices and useful information.

The list is accessible here. At the moment of this writing, the list seems to be stable and contains 14 vegetation indices, 8 water indices, 3 soil indices and 2 built-up indices. Most of them where immediately coded in OTB.

This is a real win-win approach: you give us the formulas, we give you the code.