Automating preprocessing of LANDSAT data [closed]

Question

I'm currently enrolled in a remote sensing course, using LANDSAT data.
Part of my course is about standardizing the scenes based on radiance values. I assume that because this is such a tedious task, someone has already figured out a way to automate it.

Are there any tools, Esri or otherwise that will standardize LANDSAT scenes?

Answer 1

With GRASS GIS (http://grass.osgeo.org) you can do these steps:

• Import Landsat imagery in a GRASS database using:
  • bash scripts: see page bottom at http://www.grassbook.org/examples_menu3rd.php
  • python script in GRASS-Wiki: Automated data import
• i.landsat.rgb (or i.colors.enhance in GRASS 7) - auto-enhance colors
• i.landsat.toar (addon for GRASS 6, included in GRASS 7) - convert DN to top of atmosphere radiance
• i.atcorr - correct top of atmosphere to surface reflectance
• i.landsat.acca (addon) - cloud identification
• i.landsat.dehaze (addon) - haze removal
• i.landsat.trim trims the "fringe" from the borders of Landsat images

See also here: http://grass.osgeo.org/wiki/LANDSAT

Answer 2

I found this might be useful:

http://ledaps.nascom.nasa.gov/tools/tools.html

The third package is awesome, but never tried with the first one (awaiting respond and permission from the principal).

Answer 3

I have developed an R package (LandsatLinkr), which is an automated system for processing large volumes of Landsat imagery and building spectrally consistent chronologies across MSS, TM, ETM+, and OLI sensors. See the GitHub repo: https://github.com/jdbcode/LandsatLinkr.