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Sparse and collaborative representation-based anomaly detection

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Abstract

A sparse and collaborative representation-based detector (SCRD) is proposed in this work. It uses the benefits of both sparse and collaborative representation for anomalous target detection. Anomalies compose the minority of image scene. So, sparse representation that involves a low number of dictionary’s atoms is an appropriate approach for estimating of targets. In contrast, the background pixels compose the majority of image scene. So, collaborative representation, which utilizes all atoms of dictionary, is a desired representation to model the background data. The used dictionary in sparse representation is constituted from the anomalous pixels, while the used dictionary in collaborative representation is constituted from the background ones. The proposed SCRD method has high probability of detection and low computations in comparison with several state-of-the-art anomaly detectors. The superior performance of SCRD is shown on both synthetic and real hyperspectral images.

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  1. Image Processing and Information Analysis Lab, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Maryam Imani

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  1. Maryam Imani
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Correspondence to Maryam Imani.

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Imani, M. Sparse and collaborative representation-based anomaly detection. SIViP 14, 1573–1581 (2020). https://doi.org/10.1007/s11760-020-01709-0

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  • DOI: https://doi.org/10.1007/s11760-020-01709-0

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