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Low Latency DESDynI Data Products for Disaster Response, Resource Management and Other ApplicationsWe are developing onboard processor technology targeted at the L-band SAR instrument onboard the planned DESDynI mission to enable formation of SAR images onboard opening possibilities for near-real-time data products to augment full data streams. Several image processing and/or interpretation techniques are being explored as possible direct-broadcast products for use by agencies in need of low-latency data, responsible for disaster mitigation and assessment, resource management, agricultural development, shipping, etc. Data collected through UAVSAR (L-band) serves as surrogate to the future DESDynI instrument. We have explored surface water extent as a tool for flooding response, and disturbance images on polarimetric backscatter of repeat pass imagery potentially useful for structural collapse (earthquake), mud/land/debris-slides etc. We have also explored building vegetation and snow/ice classifiers, via support vector machines utilizing quad-pol backscatter, cross-pol phase, and a number of derivatives (radar vegetation index, dielectric estimates, etc.). We share our qualitative and quantitative results thus far.
Document ID
20110015583
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Doubleday, Joshua R.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Chien, Steve A.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Lou, Yunling
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 25, 2013
Publication Date
April 11, 2011
Subject Category
Spacecraft Instrumentation And Astrionics
Meeting Information
Meeting: Information Systems for Predicting and Responding to Disasters (ISRSE 2011)
Location: Sydney
Country: Australia
Start Date: April 11, 2011
Distribution Limits
Public
Copyright
Other
Keywords
applications
onboard processing
polarimetry
SAR
disaster management
realtime
classification

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