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

Guido Lemoine,Martina Giovalli

doi:10.3390/rs5094488

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.

Highlights

The use of satellite imagery in post-disaster mapping contexts is a relatively new remote sensing discipline [1,2,3]
The digital map products are primarily aimed at direct map dissemination and integration into the geographic information system (GIS) environment of the requestor, in order to integrate the impact information with existing reference information and situation reports
We introduce the use of graphical processor units (GPU), which drastically speed up the computational processing required in template matching, leading to a robust application framework for image-to-image and image-to-reference co-registration

Summary

Introduction

The use of satellite imagery in post-disaster mapping contexts is a relatively new remote sensing discipline [1,2,3]. The rush-mode service component of EMS aims to provide post-event impact delineation and grading map products at the request of users who are active in the response and recovery phases of disaster management, typically European civil protection authorities. Provision of these post-event analysis products is a key requirement. The digital map products are primarily aimed at direct map dissemination and integration into the geographic information system (GIS) environment of the requestor, in order to integrate the impact information with existing reference information and situation reports. A description of the service set-up, the modus operandi and access to publicly releasable map products can be found in [4]

Methods

Results

Conclusion