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A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs

View Article: PubMed Central - PubMed

ABSTRACT

The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX) detector and its parallel implementation on graphics processing units (GPUs). The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments.

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Dual concentric windows model for WSSKRX anomaly detection.
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sensors-17-00441-f004: Dual concentric windows model for WSSKRX anomaly detection.

Mentions: As illustrated in Figure 4, the WSSKRX algorithm utilizes a local dual concentric windows model to detect anomalies. The inner window is used to avoid the potential target information falling into the background. And PUT reconstruction, the local kernel matrix and background covariance matrix are calculated from the pixel vectors in the outer window.


A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs
Dual concentric windows model for WSSKRX anomaly detection.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5375727&req=5

sensors-17-00441-f004: Dual concentric windows model for WSSKRX anomaly detection.
Mentions: As illustrated in Figure 4, the WSSKRX algorithm utilizes a local dual concentric windows model to detect anomalies. The inner window is used to avoid the potential target information falling into the background. And PUT reconstruction, the local kernel matrix and background covariance matrix are calculated from the pixel vectors in the outer window.

View Article: PubMed Central - PubMed

ABSTRACT

The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX) detector and its parallel implementation on graphics processing units (GPUs). The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments.

No MeSH data available.


Related in: MedlinePlus