Today's abilities of gathering and storing data are not matched by the ability to process this vast amount of data. Such examples can be found in the field of remote sensing, where new satellite missions contribute to a continuous downstream of remotely sensed data. Processing of large remotely sensed datasets has a high algorithmic complexity and requires considerable hardware resources. As many of the pixel-level operations are parallelizable, data processing can benefit from multicore technology. In this paper we use Dynamic Data Flow Model of Computation to create a processing framework that is both portable and scalable, being able to detect the number of processing cores and also capable to dynamically allocating tasks in such manner to ensure the balance of the processing load on each core. The proposed method is used to accelerate a water detection algorithm from Landsat TM data and was tested on multiple platforms with different multicore configurations.
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