Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm

Zhao, Chunhui; Yao, Xin; Huang, Bormin

doi:10.3390/rs8121011

Cited by 9 publications

(5 citation statements)

References 27 publications

(23 reference statements)

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“…In addition, the size of the sliding dual window is hard to choose, while too large a window will induce contamination from anomalies, and too small a window cannot ensure the accuracy of the background description. For this, different local background models were proposed to decrease the influence of the windows size, such as the single local area [9], [10] and multi-local area (MSAD) [11].…”

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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Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

2020

IEEE Access

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“…Besides, processor capacity (CPU or GPU), memory storage, and execution budget can be limited in operational applications [5]. In [12], a fast recursive kernel RX method was recently introduced, which processes the data in a causal manner. Although this method is fast, threshold estimation for target decision is a problematic task since the detection result is dependent on the location of the PUT in the image.…”

Section: Introductionmentioning

confidence: 99%

Randomized RX for Target Detection

Nar

Pérez-Suay

Padrón

et al. 2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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This work tackles the target detection problem through the well-known global RX method. The RX method models the clutter as a multivariate Gaussian distribution, and has been extended to nonlinear distributions using kernel methods. While the kernel RX can cope with complex clutters, it requires a considerable amount of computational resources as the number of clutter pixels gets larger. Here we propose random Fourier features to approximate the Gaussian kernel in kernel RX and consequently our development keep the accuracy of the nonlinearity while reducing the computational cost which is now controlled by an hyperparameter. Results over both synthetic and real-world image target detection problems show space and time efficiency of the proposed method while providing high detection performance.

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“…While the GRTC-RXD utilizes all processed data before the PUT as the background, LRTC-RXD regards the data contained in sliding causal array windows as the background to perform real-time detection. As these algorithms usually have an undesirable detection output by using the low-order statistics of the HSI dataset, a real-time algorithm based on the KRXD to yield better detection accuracy with much shorter processing time has been recently proposed [23]. The second such format is the band sequential (BSQ) format, which processes data samples band by band.…”

Section: Introductionmentioning

confidence: 99%

Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery

Zhao

Deng

Yan

et al. 2017

Sensors

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The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line processing of KRXD (PLP-KRXD) that can perform KRXD line by line (the main data acquisition pattern). Parallel causal sliding windows are defined to ensure the causality of PLP-KRXD. Then, with the employment of the Woodbury matrix identity and the matrix inversion lemma, PLP-KRXD has the capacity to recursively update the kernel matrices, thereby avoiding a great many repetitive calculations of complex matrices, and greatly reducing the algorithm’s complexity. To substantiate the usefulness and effectiveness of PLP-KRXD, three groups of hyperspectral datasets are used to conduct experiments.

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Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm

Cited by 9 publications

References 27 publications

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

Randomized RX for Target Detection

Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery

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