Para-GMRF: parallel algorithm for anomaly detection of hyperspectral image

The hyperspectral imager is capable of collecting hundreds of images corresponding to different wavelength channels for the observed area simultaneously, which make it possible to discriminate man-made objects from natural background. However, the price paid for the wealthy information is the enormous amounts of data, usually hundreds of Gigabytes per day. Turning the huge volume data into useful information and knowledge in real time is critical for geoscientists. In this paper, the proposed parallel Gaussian-Markov random field (Para-GMRF) anomaly detection algorithm is an attempt of applying parallel computing technology to solve the problem. Based on the locality of GMRF algorithm, we partition the 3-D hyperspectral image cube in spatial domain and distribute data blocks to multiple computers for concurrent detection. Meanwhile, to achieve load balance, a work pool scheduler is designed for task assignment. The Para-GMRF algorithm is organized in master-slave architecture, coded in C programming language using message passing interface (MPI) library and tested on a Beowulf cluster. Experimental results show that Para-GMRF algorithm successfully conquers the challenge and can be used in time sensitive areas, such as environmental monitoring and battlefield reconnaissance.