6 June 2011 Image fusion for remote sensing using fast, large-scale neuroscience models
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Abstract
We present results with large-scale neuroscience-inspired models for feature detection using multi-spectral visible/ infrared satellite imagery. We describe a model using an artificial neural network architecture and learning rules to build sparse scene representations over an adaptive dictionary, fusing spectral and spatial textural characteristics of the objects of interest. Our results with fast codes implemented on clusters of graphical processor units (GPUs) suggest that visual cortex models are a promising approach to practical pattern recognition problems in remote sensing, even for datasets using spectral bands not found in natural visual systems.
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Steven P. Brumby, Steven P. Brumby, } "Image fusion for remote sensing using fast, large-scale neuroscience models", Proc. SPIE 8064, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011, 806402 (6 June 2011); doi: 10.1117/12.884447; https://doi.org/10.1117/12.884447
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