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- W2901597741 abstract "Anomaly detection in hyperspectral images aims to separate the abnormal pixels from the background, and becomes an important application of hyperspectral data processing. Anomaly detection based on Low-Rank and Sparse Representation (LRASR) can detect abnormal pixels accurately. However, with the growth of the hyperspectral data volumes, this algorithm consumes a huge amount of time and computational resources, and needs to be improved accordingly. Spark is a distributed big data processing platform, and is applicable for complex iterative calculations, because of its powerful in-memory computation and efficient task scheduling. Based on Spark, this paper proposes a distributed and parallel LRASR (called DP-LRASR), which first segments hyperspectral images using narrow dependency of resilient distributed datasets, and afterwards, a parallel clustering algorithm is employed to improve the efficiency, remarkably. Experimental results demonstrate that DP-LRASR achieves a good speedup with high scalability, in the premise of remarkable detection accuracy." @default.
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- W2901597741 date "2018-07-01" @default.
- W2901597741 modified "2023-10-05" @default.
- W2901597741 title "A Distributed and Parallel Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation" @default.
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- W2901597741 doi "https://doi.org/10.1109/igarss.2018.8517395" @default.
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