Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery

Zhao, Chunhui; Wang, Yulei; Qi, Bin; Wang, Jia

doi:10.3390/rs70403966

Cited by 48 publications

(34 citation statements)

References 25 publications

(24 reference statements)

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“…After obtaining k m1 (n + 1) and k m2 (n + 1), k T µ (n + 1) can be updated recursively by Equation (24). This paper is inspired by the real-time RX algorithm in the literature [15,16], but it should be noted that both algorithms are different in terms of the original algorithms used to design real-time frames and the processing styles. The real-time RX algorithm is developed based on the RX algorithm, while the proposed algorithm is derived according to the nonlinear version of the RX algorithm, which is more complicated but has a higher detection accuracy.…”

Section: Local Causal Krx Detectormentioning

confidence: 99%

“…This is because, each time, more than one vector in the local window alters to make sure that the background data sample vectors in the causal sliding window only include the same data sample vectors as before. To address this issue, the literature [16] proposes a local causal sliding array window obtained from stretching out the causal matrix window. The local causal sliding array window of width w slides along with the data sample vector n r being processed, which performs first in and first out.…”

Section: Local Causal Sliding Array Windowmentioning

confidence: 99%

“…All the experiments were conducted five times and averaged to remove computer error [15,16]. Table 5 shows the total computing time of the LKRXD and the LRTC-KRXD on three hyperspectral datasets.…”

Section: Computational Analysis Of the Lrtc-krxdmentioning

confidence: 99%

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

2016

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Real-time anomaly detection has received wide attention in remote sensing image processing because many moving targets must be detected on a timely basis. A widely-used anomaly detection algorithm is the Reed-Xiaoli (RX) algorithm that was proposed by Reed and Yu. The kernel RX algorithm proposed by Kwon and Nasrabadi is a nonlinear version of the RX algorithm and outperforms the RX algorithm in terms of detection accuracy. However, the kernel RX algorithm is computationally more expensive. This paper presents a novel real-time anomaly detection framework based on the kernel RX algorithm. In the kernel RX detector, the inverse covariance matrix and the estimated mean of the background data in the kernel space are non-causal and computationally inefficient. In this work, a local causal sliding array window is used to ensure the causality of the detection system. Using the matrix inversion lemma and the Woodbury matrix identity, both the inverse covariance matrix and estimated mean can be recursively derived without extensive repetitive calculations, and, therefore, the real-time kernel RX detector can be implemented and processed pixel-by-pixel in real time. To substantiate its effectiveness and utility in real-time anomaly detection, real hyperspectral data sets are utilized for experiments.

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Section: Local Causal Krx Detectormentioning

confidence: 99%

Section: Local Causal Sliding Array Windowmentioning

confidence: 99%

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

2016

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“…This section provides a detailed analysis on the computational complexity of calculating recursive update Equations (12)- (15).…”

Section: Computational Complexity Analysismentioning

confidence: 99%

“…However, data transmission is quite time-consuming, to achieve timely detection, developing the recursive anomaly detection algorithms is important and necessary. Recently, several real-time anomaly detection methods [14][15][16][17][18][19] have been proposed. Specifically, real-time causal process of K-RXD and R-RXD detector (called as RT-CK-RXD, RT-CR-RXD) were developed in [14].…”

Section: Introductionmentioning

confidence: 99%

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

et al. 2018

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Abstract:Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the Woodbury identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors.

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Randomized Low-Rank Nonlinear RX Detector

Yapıcı,

Nar

2023

Communications in Computer and Information Science

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Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery

Cited by 48 publications

References 25 publications

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

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

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

Randomized Low-Rank Nonlinear RX Detector

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