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Keywords = KRX anomaly detection

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16 pages, 6416 KB  
Article
Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
by Chunhui Zhao, Weiwei Deng, Yiming Yan and Xifeng Yao
Sensors 2017, 17(8), 1815; https://doi.org/10.3390/s17081815 - 7 Aug 2017
Cited by 18 | Viewed by 4826
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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19 pages, 11830 KB  
Article
A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs
by Chunhui Zhao, Jiawei Li, Meiling Meng and Xifeng Yao
Sensors 2017, 17(3), 441; https://doi.org/10.3390/s17030441 - 23 Feb 2017
Cited by 8 | Viewed by 4853
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 [...] Read more.
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. Full article
(This article belongs to the Section Remote Sensors)
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