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 [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
