Using Open Source Programming with Satellite Imagery Claire Porter Polar Geospatial Center University of Minnesota
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Using Open Source Programming with Satellite Imagery
Claire Porter
Polar Geospatial Center
University of Minnesota
1. Electro-optical high-resolution commercial satellite imagery
2. PGC’s open source approach: GDAL and Python
3. Applications to other datasets
high-res imagery
• High-resolution Commercial Satellite Imagery in ice-covered regions
– 0.5m – 4m resolution
– 1 to 8 electro-optical bands
– Passive sensors
– High temporal resolution
high-res imagery
Fuel Cache
Imagery ©2011 Digital Globe, Inc.
overland traverses
Presenter
Presentation Notes
Getting supplies to pole by ground is more time consuming than by air but much more cost effective.
Imagery ©2012 Digital Globe, Inc.
field camp monitoring
Presenter
Presentation Notes
This camp is just 10s of miles up stream of the Pine Island Glacier Rift. If we can land a ski equipped LC-130 without a season to prepare a skiway NSF can save hundreds of thousands of dollars and get scientists working on site a year earlier.
icebreaker operations
1000 m Imagery ©2012 Digital Globe, Inc.
Icebreaker
McMurdo Station
Presenter
Presentation Notes
Each day the icebreaker stands down saves NSF tens of thousands of dollars.
landing site reconnaissance
landing site reconnaissance
100 m
geologic mapping
Imagery ©2012 Digital Globe, Inc.
Presenter
Presentation Notes
Geologic Mapping is a common application for commercial imagery at the poles.
emperor penguin census
Imagery ©2012 Digital Globe, Inc.
weddell seal counts
elevation from stereoscopic images
feature tracking
200m
imagery challenges
• Challenges of the imagery
– Organization
• Over 500,000 images in polar regions
• Cataloging and space requirements
– Format
• Basic spatial location – not terrain corrected
• Uncorrected DN values
• 16 bit
• NITF format
open source tools
• GDAL and Python
• Open source and freely available
• Large user base, well established and well maintained
– GDAL
• Libraries and standalone utilities
• Built with C++
• Becoming incorporated into other GIS packages
• Both raster and vector libraries
• Very good coordinate system support
• Bindings to Python, Ruby, Java, .NET, Perl, etc.
– Python
• GDAL Python bindings include Numpy/Scipy
• Benefits of GDAL with intuitive Python language
open source tools
• What you can do:
– Organization
• Query rasters and vectors for geometry and metadata
• Write info to a spatial database or vector format
• Rename and archive imagery
– Manipulation
• Orthorectify, project, and manipulate imagery
• Stack bands, pan-sharpen, mosaic, or subset multiple images
• Run the package on a linux cluster for batch processing
• Easily schedule cron jobs for repeat updates
imagery catalog
• Example 1: PGC imagery catalog
imagery processor
Input Image DEM (optional)
gdalwarp : orthorectify with RPCs
gdal_translate and gdal_merge or VRT: rescale and stretch
RPCs (if external)
VRT
Reflectance Info
gdal_translate: write to output format
Output Image
gdaladdo: compute pyramids (if geoTiff)
• Example 2: PGC image orthorectification script
radiometric correction
radiometric correction
Imagery Copyright Geoeye, Inc.
radiometric correction
mosaics
GDAL script by Dan Stahlke, GINA Imagery Copyright 2011 Digital Globe, Inc.
pan-sharpening
GDAL script by Dan Stahlke, GINA Imagery Copyright 2011 Digital Globe, Inc.
pan-sharpening
other applications
• Mosaics, Reprojection, and Subsetting: Rapidice.org
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