Automatic estimation of volcanic ash plume height using WorldView-2 imagery

We explore the use of machine learning, computer vision, and pattern recognition techniques to automatically identify volcanic ash plumes and plume shadows, in WorldView-2 imagery. Using information of the relative position of the sun and spacecraft and terrain information in the form of a digital e...

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Bibliographic Details
Main Authors: McLaren, David, Thompson, David R., Davies, Ashley G., Gudmundsson, Magnus T., Chien, Steve
Format: Report
Language:English
Published: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2012. 2013
Subjects:
sensorweb
volcanic ash
machine learning
computer vision
pattern recognition
WorldView-2
multispectral
Eyjafjallajökull
Iceland
Online Access:http://hdl.handle.net/2014/42593
Description
Summary:We explore the use of machine learning, computer vision, and pattern recognition techniques to automatically identify volcanic ash plumes and plume shadows, in WorldView-2 imagery. Using information of the relative position of the sun and spacecraft and terrain information in the form of a digital elevation map, classification, the height of the ash plume can also be inferred. We present the results from applying this approach to six scenes acquired on two separate days in April and May of 2010 of the Eyjafjallajökull eruption in Iceland. These results show rough agreement with ash plume height estimates from visual and radar based measurements. NASA/JPL

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