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

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          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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          Composite Kernels for Hyperspectral Image Classification

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            Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

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              Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                23 February 2017
                March 2017
                : 17
                : 3
                : 441
                Affiliations
                College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; hgcljw@ 123456gmail.com (J.L.); meilingmeng@ 123456hrbeu.edu.cn (M.M.); xf.yao1020@ 123456gmail.com (X.Y.)
                Author notes
                [* ]Correspondence: zhaochunhui@ 123456hrbeu.edu.cn ; Tel.: +86-451-8258-9810
                Article
                sensors-17-00441
                10.3390/s17030441
                5375727
                28241511
                28d20019-385f-46aa-bbf9-51ae416d3b45
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 January 2017
                : 20 February 2017
                Categories
                Article

                Biomedical engineering
                anomaly detection,graphics processing units (gpus),hyperspectral imaging,kernel mapping,spatial-spectral information,parallel processing

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