[GRASS-user] Derivation of Landsat 8 Tasseled Cap coefficients for certain scene in Grass

Kozlova Maria kclo at yandex.ru
Wed Jul 8 01:37:19 PDT 2015

Dear Tomas,
Thank you for response!

PR is procrustes rotation
RRN correspond to Relative Radiometric Normalization
You may look through the articles stored here


Muhammad Hasan Ali Baig did the procedure I describe with ENVI and mathlab. I'm trying to find if there is a way to do similar things with Grass

Thank for you


Best regards,

Maria V. Kozlova

  Information System Lab. (ISysLab)
  State Oceanographic Institute,
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  MOSCOW, 119034,
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  mailto:  kclo at yandex.ru 

08.07.2015, 11:22, "Tomáš Brunclík" <brunclik at atlas.cz>:
> Dear Maria,
> I have found this note by chance, because I do not follow all the traffic in the GRASS mailing list.
> I have to admit I have some difficulties to grasp the meaning of the steps you describe, for example the abbreviations PR and RRN, will have to read the articles you mention first. Generally, if the process includes radiometric normalization, and correlation-based normalization is applicable, use of my script should be possible.
> But for now, I have found full text of the article Baig et al., 2014, on Researchgate: https://www.researchgate.net/publication/262005316_Derivation_of_a_tasselled_cap_transformation_based_on_Landsat_8_at-_satellite_reflectance
> Hope it  helps.
> Tomas B.
> Dne 4.7.2015 v 23:26 Kozlova Maria napsal(a):
>> Dear colleagues, Some time ago I asked if it is possible to derive custom TC coefficients by Grass and described the first algorithm for derivation of TC coefficients in attached file.   The steps needed to derive TC coefficients for multispectral image  are not completely uniform. Here is the description of second algorithm I'd like to follow for derivation of TC coefficients   The second recipe, based on TM tasseled cap transformation.  (Suggested by Huang et al., 2002, Lobster and Cohen, 2007 and Baig et al., 2014) 1.   Selection of ‘dummy target' (as it is called in a paper by Baig et al., 2014) by applying of TM TC-coefficients directly to OLI or ETM+ data   Here, in Grass, I can choose sensor type TM for OLI data, so this step is seems to be possible to do in Grass by i.tasscap.   2. PCA (it is clear for Grass and easily can be done in the images of interest by i.pca). 3.   PR rotation of PCA matrix seems to be the most problematic step. Generally it can be done by the finding of best alignment of pca matrix created in step2 with previously created (in step1) dummy target. Such a way: - more then 4 TC components should be included to i.tasscap - some kind of raster comparison and relative normalization must be done, I guess, but I don't know, what tool exactly should be used.   A little question directly to Tomas Brunclic:   Dear Tomas: what do you think about the application of your script on RRN (i.grid.correl.atcor....) in this case? I would be very appreciate for your answer.   4. Get TC-coefficients by applying i.mapcalc to original Landsat data and resulting PR rotated data   Such a way, TC coefficients arise from: PCA axes rotation coefficients PR of PCA matrix coefficients.       P.S.: TCT of Landsat MSS data can be done as I see only by Jackson's algorithm. So still hoping for any help with these two TCT algorithms.
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