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Sensors 2017, 17(8), 1815; https://doi.org/10.3390/s17081815

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

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
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Received: 30 June 2017 / Revised: 26 July 2017 / Accepted: 4 August 2017 / Published: 7 August 2017
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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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 (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. View Full-Text
Keywords: hyperspectral imagery; KRX anomaly detection; real-time algorithm; progressive line processing; the causal sliding window hyperspectral imagery; KRX anomaly detection; real-time algorithm; progressive line processing; the causal sliding window
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Zhao, C.; Deng, W.; Yan, Y.; Yao, X. Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery. Sensors 2017, 17, 1815.

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