2012 ENVI User Group: COSI-Corr User Presentations
At the 2012 ENVI User Group, Sebastien Leprince, California Institute of Technology, will be presenting “COSI-Corr: Principles of Sub-Pixel Correlation” and Francois Ayoub, California Institute of Technology, will be presenting “COSI-Corr: Application of Sub-pixel Correlation for Change Detection”.
An orthorectified image, or orthophoto, is one where each pixel represents a true ground location and all geometric, terrain, and sensor distortions have been removed to within a specified accuracy. Orthorectification transforms the central perspective of an aerial photograph or satellite-derived image to an orthogonal view of the ground, which removes the effects of sensor tilt and terrain relief. As a result, scale is constant throughout the orthophoto, regardless of elevation, thus providing accurate measurements of distance and direction. The need for this type of corrected image is very important to geospatial professionals, who need to combine orthophotos with other spatial data in a GIS for city planning, natural disaster rescue efforts, and other tasks.
COSI-Corr is a plug-in for ENVI and is freely available from the California Institute of Technology’s Tectonics Observatory. COSI-Corr provides tools to accurately orthorectify, co-register, and correlate optical remotely sensed images to retrieve ground surface deformation from multi-temporal images. It can also be a valuable tool for many other change detection applications requiring accurate co-registration of images, such as measuring glacier flows or landslides.
Using accurate digital elevation models with global coverage, such as SRTM, users can achieve subpixel change detection measurements using the COSI-Corr methodology in ENVI. With COSI-Corr, it is possible to measure local displacements between images, even when those images are from different instruments and/or at different resolutions.
The methodology used by COSI-Corr corrects pointing inaccuracies in push-broom satellites and aerial images for accurate subpixel image coregistration. This accuracy also allows for the displacement field between multitemporal images to be estimated accurately. With COSI-Corr, users are able to investigate a variety of geomorphic and seismotectonic processes.
You can learn more about the work of Sebastien Leprince, Froancois Ayoub, and about COSI-Corr by visiting: http://www.tectonics.caltech.edu/slip_history/spot_coseis/ and http://www.imaginlabs.com/
What application of COSI-Corr do you think is the most valuable to remote sensing science?