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Remote sensing image information mining with HPC cluster and DryadLINQ

Our capabilities for collecting remote sensing images have greatly outpaced our abilities to analyze and retrieve information from the image databases. This paper presents a distributed framework for information mining from multi-dimensional remotely sensed images using Windows High Performance Computing (HPC) Servers and Dryad distributed computing engine. Land cover and land use types are classified by Support Vector Machines (SVM) and stored in an object-oriented database with region quad-tree indices. DryadLINQ queries, an extended version of the LINQ programming model, are developed for retrieving land cover distribution information and detect the changes of each land cover type at multi levels. A HPC cluster with sixteen computing nodes is implemented and the experiments are conducted on a time series Landsat Thematic Mapper (TM) images. The results show the effectiveness of the framework and its potentials in other remote sensing applications.