Parallel evolution of image processing tools for multispectral imagery

摘要

We describe the implementation and performance of a parallel, hybrid evolutionary-algorithm-based system, which optimizes image processing tools for feature-finding tasks in multi-spectral imagery (MSI) data asets. Our system uses an integrated spatio-spectral approach and is capable of combining suitably-registered data from different sensors. We investigate the speed-up obtained by parallelization of the evolutionary process via multiple processors (a workstation cluster) and develop a model for prediction of run-times for different numbers of processors. We demonstrate our system on Landsat Thematic Mapper MSI, covering the recent Cerro Grande fire at Los Alamos; NM, USA.