13 October 2008 Anomalies detection approach using projection pursuit in hyperspectral image based on parallel genetic algorithm
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Abstract
This anomalies detection approach seeks the directions that maximize the projection index, so as to gain the anomalies structure information. Using genetic algorithm in this approach can search accurate optimal projection directions, but it's a computation-intensive task. So, a parallel algorithm under distributed memory system was presented. The projection directions were searched efficiently by parallel genetic algorithm model, and the projection directions' precision was guaranteed by using a strengthened terminal qualification. Then, the detected anomaly components were wiped off by projecting the data onto the subspace orthogonal to the previous projection directions, and the other anomalies were searched in the residual space. The final task of projection and objects segmentation was also completed in parallel. Using an OMIS hyperspectral data to test the parallel algorithm's performance under an eight-node cluster, the process time reduced from 15 minutes to 2.8 minutes. The results show the validity and comparative good parallel efficiency.
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Wei Wang, Huijie Zhao, Chao Dong, "Anomalies detection approach using projection pursuit in hyperspectral image based on parallel genetic algorithm", Proc. SPIE 7129, Seventh International Symposium on Instrumentation and Control Technology: Optoelectronic Technology and Instruments, Control Theory and Automation, and Space Exploration, 71290L (13 October 2008); doi: 10.1117/12.807630; https://doi.org/10.1117/12.807630
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