Re: [GRASS-user] Workflow of a classification project with orthophotos
On 31/07/08 20:39, Nikos Alexandris wrote: Any Open Source alternatives for image segmentation? SAGA GIS has some segmentation algorithms included: http://www.saga-gis.uni-goettingen.de/html/index.php Moritz ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Workflow of a classification project with orthophotos
Nikos Alexandris pisze: how do Open Source Professionals image normalisation for aerial photos... let's say 300 photos? I cannot imagine that people sit-down and extract psuedoinvariant targets for 300 photos (except they are payed a lot for that). Nikos, Have you looked at OSSIM? Not that I'm sure it provides the tool, but seems likely. -- Maciej Sieczka www.sieczka.org ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Workflow of a classification project with orthophotos
On Fri, 2008-08-01 at 12:01 +0200, Maciej Sieczka wrote: Nikos Alexandris pisze: how do Open Source Professionals image normalisation for aerial photos... let's say 300 photos? I cannot imagine that people sit-down and extract psuedoinvariant targets for 300 photos (except they are payed a lot for that). Nikos, Have you looked at OSSIM? Not that I'm sure it provides the tool, but seems likely. Thanks for the suggestion Maciej. OSSIM sounds very promising (from what I've read so far). Till today I never managed to get OSSIM running under Ubuntu. I've seen it only under some win-boxes and partially running under wine. But it's probably an adventure to compile it properly. ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Workflow of a classification project with orthophotos
On Fri, 2008-08-01 at 15:29 +0200, G. Allegri wrote: A collegue sent me this ticks to run OSSIM on Ubuntu 7.10: http://trac.osgeo.org/ossim/wiki/Ubuntu-7.10Build after every make make install, give a ldconfig and start another shell to continue the compilation. in /etc/ld.so.conf.d/ I've exported the following libs : /usr/lib /usr/local/lib /home/sasha/GIS/ossim/ossim/lib within a file ossim.conf -- Yet I've never found the time to try it... Giovanni I've been trying in the past and today again... but no luck. It's not an easy process. I wonder how this affects the status of OSSIM as an osgeo tool? Shouldn't all osgeo packages be installable in most major platforms? Well, this is a question for another list. Cheers, Nikos ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Workflow of a classification project with orthophotos
After examining the mosaic I found multiple and big differences. I conclude that the producer did not perform any radiometric nor topographic corrections. It is a collage and not a mosaic :-) Is this the way it should be? Thank you, Nikos ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Workflow of a classification project with orthophotos
Nikos: Performing relative radiometric normalization is a *requirement* of applying a single classification to multiple images (also for change detection). Unfortunately, it is not an algorithm that is available (to my knowledge), out-of-the-box, on ANY remote sensing platform (GRASS, ENVI, etc.). However, you can do the radiometric normalization yourself -- the idea is that pixels in the overlap zone between two images which are invariant (e.g. have not changed in structure, spectral properties or, in more complex architectures like trees, sun angle) should be linearly related to their counterpart in the other image. Assuming this, you can either manually choose a set of psuedoinvariant targets (pairs of pixels which are at the same location and are not changing) between the two images, and calculate an orthogonal regression to generate gains and offsets. One of those images, therefore, becomes your reference and the other one your target. The gains/offsets are applied to the target image. There are automated algorithms for doing the pseudoinvariant pixel selection (search for radiometric normalization remote sensing on google scholar), or if you assume that the images do not change between dates and are WELL rectified to one another, you can extract the ENTIRE overlap zone between the two images and calculate the regressions based on those. This last suggestion is probably the fastest, but also incurs the most error and I wouldn't neccessarily recommend it. This would be a VERY good algorithm to add to GRASS -- if anyone is interested in pursuing coding this, I can help design the algorithm (including which are the best automated invariant target selection algorithms). --j Nikos Alexandris wrote: After examining the mosaic I found multiple and big differences. I conclude that the producer did not perform any radiometric nor topographic corrections. It is a collage and not a mosaic :-) Is this the way it should be? Thank you, Nikos ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user -- Jonathan A. Greenberg, PhD Postdoctoral Scholar Center for Spatial Technologies and Remote Sensing (CSTARS) University of California, Davis One Shields Avenue The Barn, Room 250N Davis, CA 95616 Cell: 415-794-5043 AIM: jgrn307, MSN: [EMAIL PROTECTED], Gchat: jgrn307 ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Workflow of a classification project with orthophotos
On Thu, 2008-07-31 at 11:17 -0700, Jonathan Greenberg wrote: Nikos: Performing relative radiometric normalization is a *requirement* of applying a single classification to multiple images (also for change detection). Unfortunately, it is not an algorithm that is available (to my knowledge), out-of-the-box, on ANY remote sensing platform (GRASS, ENVI, etc.). However, you can do the radiometric normalization yourself -- the idea is that pixels in the overlap zone between two images which are invariant (e.g. have not changed in structure, spectral properties or, in more complex architectures like trees, sun angle) should be linearly related to their counterpart in the other image. Assuming this, you can either manually choose a set of psuedoinvariant targets (pairs of pixels which are at the same location and are not changing) between the two images, and calculate an orthogonal regression to generate gains and offsets. One of those images, therefore, becomes your reference and the other one your target. The gains/offsets are applied to the target image. There are automated algorithms for doing the pseudoinvariant pixel selection (search for radiometric normalization remote sensing on google scholar), or if you assume that the images do not change between dates and are WELL rectified to one another, you can extract the ENTIRE overlap zone between the two images and calculate the regressions based on those. This last suggestion is probably the fastest, but also incurs the most error and I wouldn't neccessarily recommend it. This would be a VERY good algorithm to add to GRASS -- if anyone is interested in pursuing coding this, I can help design the algorithm (including which are the best automated invariant target selection algorithms). --j Jonathan, thank you very much for your reply. I've done my homework and I already read previous posts of yours as well as from other people. I already know this process as I performed it on a change detection project [1] It's a time consuming process even for just 2 images. My real BIG question is: how do Open Source Professionals image normalisation for aerial photos... let's say 300 photos? I cannot imagine that people sit-down and extract psuedoinvariant targets for 300 photos (except they are payed a lot for that). As I wrote the Mosaic that I work on is a MESS. And the people do not provide the original data. So I don't have any overlapping zones at all :D So I forget the normalisation anyway! The next possible solution for mapping my forest gaps (see first and second mail of mine) is, I think, to extract only segments somehow and the identify the forest gaps visually. The segmentation would save me since it's faster to recognise homogenous gaps that way. Now I am kind of disappointed since I can't get i.smap do this segmentation-solo task. And of course I cannot collect training samples for 300 photos. Any Open Source alternatives for image segmentation? . [1] Details: I performed an empirical image normalisation, that is a regression-based normalisation, for burned area mapping with MODIS satellite imagery, a pre-fire and a post-fire image more or less the way you describe it. I intend to participate in FOSS4G in South Africa (although other difficulties do not allow me to participate in the upcoming conference). I have a step-by-step document with more than 120 pages and I don't know anybody with experience who would like to have a look at it so it's still under heavy corrections :-) P.S. If anyone is interested to have a look in my step-by-step document I invite him for free vacation in my home in Central Greece :D ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Workflow of a classification project with orthophotos
I am still struggling with this. In theory it sounds easy but when it comes to the point it's quite hard considering that we don't have the raw data. Any other ideas? Thank you, Nikos On Wed, 2008-07-16 at 16:50 +0200, Nikos Alexandris wrote: [...] My workflow 1. Stretch colour orthophotos (8-bit R,G and B bands) from 0 to 255 values (weither with GDAL or import in GRASS' database and stretch inside the DB) 2. Visually identify the different groups of images taken more or less at the same time This sounds too difficult but we don't have the metadata (i.e. date of acquisition to reasonably group the tiles based on this information). I have some vector of interest areas which correspond to biger admnistrative areas (images are from West-Central Germany, groups are something like koblenz, trier, simmern and more). 3. Split the mosaic in the groups that include photos that present less colour differences 4. Sampling 5. Segmentation with i.smap 6. Use r.texture as I think it will boost the accuracy of the classification 7. Classify 8. Some handwork to improve sampling 9. Re-Run segmentation, classification 10. Handwork to correct obvious errors 11. Voila the power of GFOSS ;-) ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
[GRASS-user] Workflow of a classification project with orthophotos
Dear GRASSers, I would like to have a confirmation that my feet are on the ground when I try to realise the following work-flow with G-FOSS. I want to classify forest gaps out of orthophotos (...actually it's not my job but I want to help somebody who intented to do all by hand or accept what others say that this task cannot be done unless one utilises commercial tools... !) I have more than 300 tiles of a mosaic composed by on aerial imagery. Unfortunately it is a mixture of different acquisitions (date) and has significant contrast differences in some regions. My class-scheme would be gaps, shadows of tree-stands withing the gaps water, vegetation, urban surfaces. I can not perform any normalisation the way I know it for some number of pictures (e.g. for 3,4 satelliteimagery). First of all there are no overlapping areas and I am not aware (practically) of any other method to perform a colour balance. Anyone struggling with normalisation, colour balancing issues without having the meta-data (date of acquisition) nor the raw data? My workflow 1. Stretch colour orthophotos (8-bit R,G and B bands) from 0 to 255 values (weither with GDAL or import in GRASS' database and stretch inside the DB) 2. Visually identify the different groups of images taken more or less at the same time I have some vector of interest areas which correspond to biger admnistrative areas (images are from West-Central Germany, groups are something like koblenz, trier, simmern and more). 3. Split the mosaic in the groups that include photos that present less colour differences 4. Sampling 5. Segmentation with i.smap 6. Use r.texture as I think it will boost the accuracy of the classification 7. Classify 8. Some handwork to improve sampling 9. Re-Run segmentation, classification 10. Handwork to correct obvious errors 11. Voila the power of GFOSS ;-) Cheers, Nikos ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
