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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images
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A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

机译：基于低秩稀疏表示的分布式并行高光谱图像异常检测算法

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摘要

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.
机译：异常检测旨在将异常像素与背景分离，并已成为遥感高光谱图像处理的重要应用。基于低秩和稀疏表示（LRASR）的异常检测方法可以准确检测异常像素。但是，随着高光谱图像存储库的大量增加，此类技术会消耗大量时间（主要是由于涉及大量矩阵计算）。在本文中，我们通过根据MapReduce模型重新设计LRASR的关键运算符，以在云计算架构上加速LRASR，提出了一种新颖的分布式并行算法（DPA）。在Spark上探索并并行执行独立的计算运算符。具体来说，我们以适当的格式重构高光谱图像，以进行有效的DPA处理，设计优化的存储策略，并开发一种预合并机制以减少数据传输。此外，还提出了重新分配政策以提高DPA的效率。我们的实验结果表明，除了保持相似的精度外，新开发的DPA在加速LRASR时也实现了非常高的加速比。此外，我们提出的DPA已显示可随着计算节点的数量扩展，并能够处理涉及大量数据的大型高光谱图像。

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