Paper Info
Title
Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
Abstract
The increasing availability of satellite imagery acquired by existing and new sensors allows a wide variety of new applications that depend on the use of diverse spectral and spatial resolution data sets. One of the pre-conditions for the use of hybrid image data sets is a consistent geo-correction capacity. We demonstrate how a novel fast template matching approach implemented on a graphics processing unit (GPU) allows us to accurately and rapidly geo-correct imagery in an automated way. The key difference with existing geo-correction approaches, which do not use a GPU, is the possibility to match large source image segments (8,192 by 8,192 pixels) with relatively large templates (512 by 512 pixels) significantly faster. Our approach is sufficiently robust to allow for the use of various reference data sources. The need for accelerated processing is relevant in our application context, which relates to mapping activities in the European Copernicus emergency management service. Our new method is demonstrated over an area northwest of Valencia (Spain) for a large forest fire event in July 2012. We use the Disaster Monitoring Constellation's (DMC) DEIMOS-1 and RapidEye imagery for the delineation of burnt scar extent. Automated geo-correction of each full resolution image set takes approximately one minute. The reference templates are taken from the TerraColor data set and the Spanish national ortho-imagery database, through the use of dedicated web map services. Geo-correction results are compared to the vector sets derived in the Copernicus emergency service activation request.
Year
DOI
Venue
2013
10.3390/rs5094488
REMOTE SENSING
Keywords
Field
DocType
geo-correction,template matching,TerraColor,ortho-imagery,DEIMOS-1,RapidEye,WMS,GPU,cuFFT,forest fire,Copernicus,emergency mapping
Template matching,Computer vision,Data set,Satellite imagery,Remote sensing,Hybrid image,Artificial intelligence,Pixel,Geology,Graphics processing unit,Image resolution,Orthophoto
Journal
Volume
Issue
ISSN
5
9
2072-4292
Citations 
PageRank 
References 
7
0.65
13
Authors
2
Name
Order
Citations
PageRank
Guido Lemoine122736.45
Martina Giovalli281.03