Abstract
In this paper, we propose a novel anomaly targets detection algorithm baesd on information processing method and KRX anomaly detector. It use fully nolinear feature and decrease bands redundancy for hyperspectral imagery. Firstly, the original hyperspectral imagery is clustered by a new clustering method, i.e. k-clustering of particle swarm optimization. Then, we extract a largest fourth-order cumulant value in every class, and constitute a optimal band subset. Finally, the KRX detector is used on the band subset to get anomaly detection results. The simulation results demonstrate that the proposed PSOC-KRX algorithm outperforms the other algorithm, it is higher precision and lower false alarm rate.
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Cheng, B., Guo, Z. (2013). Anomaly Detection in Hyperspectral Imagery Based on PSO Clustering. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_22
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DOI: https://doi.org/10.1007/978-3-642-38703-6_22
Publisher Name: Springer, Berlin, Heidelberg
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