A Low-Rank and Sparse Matrix Decomposition- Based Dictionary Reconstruction and Anomaly Extraction Framework for Hyperspectral Anomaly Detection

Xu, Yichu; Du, Bo; Zhang, Liangpei; Chang, Shizhen

doi:10.1109/lgrs.2019.2943861

Cited by 29 publications

(19 citation statements)

References 18 publications

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“…3) Detection Performance Evaluation: In this section, some traditional anomaly target detection algorithms are used for a comparison purpose, including global RXD [10], kernel RXD [36], SSRXD [13], collaborative representation detector [37], LSDRAD [21], SC-CNMF anomaly detector [27], BACON [12], and EDLRaSMD [20]. Then, the eight comparative algorithms and the PRLRaSAD were implemented for the five data sets.…”

Section: B Experimental Results and Discussionmentioning

confidence: 99%

“…The final update rules of the entire solution for the PRL-RaSMD method combine (21) and (22). Three matrices are updated alternately.…”

Section: B Prlrasad Methodsmentioning

confidence: 99%

“…The anomaly targets are included in the sparse matrix. Some LRaSMD-based algorithms have demonstrated a strong detection performance, such as the LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection [14], a novel low-rank and sparse decomposition [19], Euclidean distancebased LRaSMD (EDLRaSMD) [20], the LRaSMD-based dictionary reconstruction and anomaly extraction framework for hyperspectral anomaly detection (LSDRAD) [21], and so on [22]. The advantages of the LRaSMD-based algorithms can be summarized as follows.…”

mentioning

confidence: 99%

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An Improved Low Rank and Sparse Matrix Decomposition-Based Anomaly Target Detection Algorithm for Hyperspectral Imagery

Zhang

Fan

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Anomaly target detection has been a hotspot of the hyperspectral imagery (HSI) processing in recent decades. One of the key research points in the HSI anomaly detection is the accurate descriptions of the background and anomaly targets. Considering this point, we propose a novel anomaly target detector in this article. Improving upon the low-rank and sparse matrix decomposition (LRaSMD) approach, the proposed method assumes that the lowrank component can be described as the parts-based representation. Parts refer to the various ground objects in HSI. A new update rule of the low-rank component and sparse component is proposed. The proposed approach can be divided into three main steps: first, further refining the low-rank component in the LRaSMD model as the parts-based representation. Then, the HSI is decomposed as three parts: the product of the basis matrix and coefficient matrix, sparse matrix, and noise. Second, the basis vectors matrix, coefficient matrix, and sparse matrix are solved by the new update rules. Third, since the anomaly targets exist in the sparse matrix, the sparse matrix is thus employed to detect the anomaly targets. The experiments implemented for five data sets demonstrate that the proposed algorithm achieved a better performance than the traditional algorithms.

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Section: B Experimental Results and Discussionmentioning

confidence: 99%

“…The final update rules of the entire solution for the PRL-RaSMD method combine (21) and (22). Three matrices are updated alternately.…”

Section: B Prlrasad Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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An Improved Low Rank and Sparse Matrix Decomposition-Based Anomaly Target Detection Algorithm for Hyperspectral Imagery

Zhang

Fan

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…To avoid the extraction of background pixels, some methods utilize characteristics of low-rank and a sparse background to detect anomalies. With the assumption of similarity of spectral features and spatial features of the background, many of the latest anomaly detection (AD) attempt to learn a dictionary and detected anomaly by reconstruction error [23]- [27]. Other methods apply slowly varying signal analysis [28], matrix decomposition [29], [30] or optimal filters [31], [32] to detected targets.…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised clustering, as a type of self-organized classification method, only extracts most background pixels which supply a relatively accurate measurement for anomaly salience [22] and spatial-spectral similarity [25]. It also supplies a relatively accurate data-set to construct a background dictionary [24], [26], [27]. Iterative computations is another effective method for solving this kind of problem.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

2020

IEEE Access

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Anomaly detection based on clustering is a classic method that supplies a simplified manner to describe a cluttered background. However, traditional clustering methods need to know the number of clusters in advance and attempt to classify all the background pixels at one time. In addition, compared with large background clusters, small clusters are hard to discriminate due to their small populations. In this paper, an anomaly detection method based on adaptive background extraction is proposed. We apply an unsupervised clustering method to determine the cluster centers according to only the similarity of the spectral signature. To reduce the influence of the population, we propose to extract background clusters iteratively. Every iteration, we only cluster the larger clusters and extract them from the data-set. In the next iteration, the remaining pixels are clustered again. Without interference from the larger clusters, the centers of smaller clusters will appear obviously. The clustering process stop when the number of remaining pixels nears the appearance probability of anomaly (generally approximately 10%∼20%). Then, only anomalies and few background pixels remain to test. Finally, every extracted background cluster, as a viewer, is applied to measure the anomaly salience of the test pixels. In addition, a weighted summation is proposed to fuse the different salience values from different viewers. Simulation experiments on two sets of real data are presented to demonstrate the superiority of the proposed method.

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Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

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The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.

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A Low-Rank and Sparse Matrix Decomposition- Based Dictionary Reconstruction and Anomaly Extraction Framework for Hyperspectral Anomaly Detection

Cited by 29 publications

References 18 publications

An Improved Low Rank and Sparse Matrix Decomposition-Based Anomaly Target Detection Algorithm for Hyperspectral Imagery

An Improved Low Rank and Sparse Matrix Decomposition-Based Anomaly Target Detection Algorithm for Hyperspectral Imagery

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

Anomaly detection using edge computing in video surveillance system: review

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