Distributed geo-rectification of satellite images using Grid computing

Abstract

Grid computing seeks to aggregate computing resources within an enterprise and leverage on resources you don't own for compute-intensive applications. Geo-rectification is a process for correcting spatial location and orientation of a satellite image. This paper focuses on the parallelization of the computeintensive satellite image geo-rectification problem on a cluster grid. We discuss our approach to data and task partitioning, visualization technique and the archival of data. The computational tasks include wrapping satellite positional data to compensate the earth curvature, and consist of several steps such as image re-sampling, resolution conversion and image matching. Experimental results obtained using commodity PCs are discussed. IntroductionGrid computing [11,13] seeks to efficiently coordinate the sharing of geographically distributed computing resources, thereby bringing supercomputing power to its users. Unlike cluster computing [4,5,26] that is more constrained to computation on a local area networked of processors, grid computing enables applications to utilize resources that are spread across wide area networks. , architecture for matching grid application requirements to a set of heterogeneous grid resources is proposed. Our grid middleware, called ALiCE (Adaptive and scaLable Internet-based Computing Engine), is a portable software technology for developing and deploying general-purpose grid applications and systems. ALiCE aggregates and virtualises computer resources on the Internet/intranet into one computing environment through a platform-independent consumer-producer resource-sharing model, and

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