Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts

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...

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Bibliographic Details
Main Authors: Martina Giovalli, Guido Lemoine
Format: Article
Language:English
Published: MDPI AG 2013-09-01
Series:Remote Sensing
Subjects:
WMS
GPU
Online Access:http://www.mdpi.com/2072-4292/5/9/4488
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spelling doaj-a83c6834c6d943d880dc69a2a6ae37632020-11-24T23:48:30ZengMDPI AGRemote Sensing2072-42922013-09-01594488450210.3390/rs5094488Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping ContextsMartina GiovalliGuido LemoineThe 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.http://www.mdpi.com/2072-4292/5/9/4488geo-correctiontemplate matchingTerraColorortho-imageryDEIMOS-1RapidEyeWMSGPUcuFFTforest fireCopernicusemergency mapping
collection DOAJ
language English
format Article
sources DOAJ
author Martina Giovalli
Guido Lemoine
spellingShingle Martina Giovalli
Guido Lemoine
Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
Remote Sensing
geo-correction
template matching
TerraColor
ortho-imagery
DEIMOS-1
RapidEye
WMS
GPU
cuFFT
forest fire
Copernicus
emergency mapping
author_facet Martina Giovalli
Guido Lemoine
author_sort Martina Giovalli
title Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
title_short Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
title_full Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
title_fullStr Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
title_full_unstemmed Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
title_sort geo-correction of high-resolution imagery using fast template matching on a gpu in emergency mapping contexts
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2013-09-01
description 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.
topic geo-correction
template matching
TerraColor
ortho-imagery
DEIMOS-1
RapidEye
WMS
GPU
cuFFT
forest fire
Copernicus
emergency mapping
url http://www.mdpi.com/2072-4292/5/9/4488
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