[GRASS-user] value differences between landsat images
greenberg at ucdavis.edu
Mon May 19 21:36:37 EDT 2008
I think you are talking about radiometric normalization between images.
This is performed by first extracting values of "pseudoinvariant
features" ("PIFs") in the overlap zones between images -- these are
features which should be the same, spectrally. If you have a set of
band values of PIFs from images A and B, you can create a regression
between the two of them (you should be able to get an R2 >> .9 for all
bands), and then apply the regression coefficients to one of the images
(this assumes the other image is treated as the "reference" image,
typically an image that has been converted to at-the-ground reflectance
via some atmospheric correction first (e.g. i.atcorr). This gets the
two images in the same radiometric scale, which allows you to use, for
instance, the same threshold value two differentiate water between the
two images (incidentally, a low band 5 or 7 should be enough to
distinguish water from everything else except cloud shadows).
Typically the order of ops is:
1) Perform image-to-image orthorectification for all images (so the
overlap zones are +/- 1 pixel registered to one another).
2) Perform atmospheric correction of one image ("reference image").
3) Choose PIFs between images that cover a range of bright/dark
reflectance values for each band (e.g. soil, water, vegetation), extract
data to a stats package, remove outliers, calculate band-to-band
gain/offsets between reference image and other uncorrected images.
4) Apply gain/offsets to the uncorrected images.
Google scholar: radiometric normalization "remote sensing" for some
pubs on this.
Juan Manuel Barreneche wrote:
> Hello grass users!
> This may be a very technical question, but i hope somebody knows the answer
> I'm currently working with landsat 5 images, trying to identify water
> bodies with an algorithm that i found on the web (*). The thing is,
> many calculations are made in the process, including NDVI and
> normalization of the different bands, but there are important
> differences between landsat images, even with adjacent ones. This
> differences in values can be > 30, for the same kind of terrain, e.g.:
> r.what -f input=223_082_B east_north=502773.212142,6416651.23944
> 502773.212142 | 6416651.23944 || 83 |
> r.what -f input=223_083_B east_north=502789.528784,6415683.11873
> 502789.528784 | 6415683.11873 || 49 |
> (in this example i used the r.what function in both images, in some
> area that is repeated but slightly moved from one image to the other)
> This would obviously cause a difference in the calculations that i
> want to run, and so can be a big problem for me, but that depends of
> how these differences are produced, for which i imagine two scenarios:
> 1. In one hand, if the different values are generated by a constant
> that multiplies all the values in the image (i.e., all the "real"
> values), then the problem is solved, because said constant would be
> eliminated in the calculations (ej. in the NDVI formula, it would be
> divided by itself).
> 2. On the other hand, if the different values are generated by a
> constant that is summed to all the values, then i'm really lost,
> because i can't imagine a way around to solve the problem.
> Maybe some other scenario is really ocurrying (maybe a combination of
> both??). I made a small test with two adjacent images (by comparing
> many points like in the example), and it looked like the first
> scenario, but with great uncertainty.... (In practice i can't do the
> same exploration for all the images that i have, because of the error
> involved and the time that consumes)
> If anyone have any clue that can help i'd be very grateful,
> Juan M. Barreneche
> Zoología de Vertebrados,
> Facultad de Ciencias
> Universidad de la República,
> Montevideo, Uruguay
> (*): DETECÇÃO E DISCRIMINAÇÃO DE SOMBRAS, NUVENS E CORPOS D'ÁGUA EM
> IMAGENS DE SENSORIAMENTO REMOTO. Airton Marco Polidorio, Nilton
> Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Franklin César Flores.
> Revista Brasileira de Cartografia # 58/03, Dec 2006. (ISSN 1808-0936)
> grass-user mailing list
> grass-user at lists.osgeo.org
Jonathan A. Greenberg, PhD
Center for Spatial Technologies and Remote Sensing (CSTARS)
University of California, Davis
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Davis, CA 95616
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