A Distributed and Parallel Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

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.