Intelligent Classification of Large-Scale Remotely Sensed Hyperspectral Images using Multi-GPU Computing

Image classification is one of the most popular tasks used to analyze remote sensing signatures (RSS) of a geographical region from remotely sensed hyperspectral images. However, the high dimensionality of such hyperspectral images raises a series of new challenges. In this paper, a new approach for real-time intelligent classification of large-scale hyperspectral imagery which aggregates Fuzzy logic and the fused weighted order statistic (WOS) with the minimum distance to mean (MDM) techniques using commodity graphics processing units (GPUs) is addressed. Within this context, intelligent image processing methods are algorithmically adapted via parallel computing techniques and efficiently implemented in two NVIDIA Tesla C2075 GPUs. Experimental results demonstrate how such unification reduces drastically the computational load of the real-world hyperspectral classification tasks resulting in efficient numerical algorithms suitable for real-time multi-GPU-adapted implementation.