| dc.description.abstract | The rapid increase in the number and in the spatial resolution of aerial and orbital Earth observation systems is generating a huge amount of remote sensing data that need to be readily transformed into useful information for policy and decision makers. A possible approach to tackle the demand for image interpretation tools that can deal efficiently with very large volumes of data is to employ data analysis methods based on distributed computing. This paper presents an object-based, remote sensing image interpretation application executed over cloud-computing infrastructure. The application is implemented with InterCloud, a novel image interpretation platform designed to run on computer grids (physical clusters or cloud-computing infrastructure). The application described in this paper is a land cover/land use classification of a pansharpened GeoEye-1 image, with 19k by 23k pixels. The image covers an area of the municipality of Goianésia, in Goiás State, Brazil. The site contains sparse urban areas intermixed with rural areas and natural patches of the Brazilian Cerrado biome. Eleven classes of objects, including urban, rural and Cerrado reminiscent targets were considered. In addition to the accuracies of the classification result, in this work we evaluate the scalability capability of InterCloud by performing different runs of the application with different configurations of the cloud infrastructure, in which we vary the number of computing nodes. | en_us |
