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.

CBERS 2B satellite imagery was acquired in the period 2007-2010, at 2.5 m (panchromatic) and 20 m (multispectral) spatial resolution, respectively. Much of the archived data is available, free of charge, via the Brazilian space organization INPE. However, to be suitable for wide area mapping at scales 1:50,000 to 1:100,000 scales (or better), errors in geo-location, which range from several 100s of meters to several kilometers, must be corrected for each individual image frame. We demonstrate a novel method which deploys very fast template matching using GPU accelerated convolution. The method can be used with template samples from a globally consistent reference set. We demonstrate this with the use of the Terracolor LANDSAT product over Brazil and with Bing maps extracts for some urban areas in panchromatic scenes. The main advantage of the method is the possibility to use a large number of templates of a significant size (256 by 256, or 512 by 512 pixels). Processing time remains below 1 minutes per frame, which includes template selection via a WMS service. The result of the match is supplied as a list of ground control points, which we store in the VRT format, allowing the use of GDAL for transformation. We discuss implementation details and show how we can apply the method to other sensor combinations as well.