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

Chunhui Zhao; Weiwei Deng; Yiming Yan; Xifeng Yao

doi:10.3390/s17081815

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

Sensors (Basel). 2017 Aug 7;17(8):1815. doi: 10.3390/s17081815.

Authors

Chunhui Zhao¹, Weiwei Deng², Yiming Yan³, Xifeng Yao⁴

Affiliations

¹ College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. zhaochunhui@hrbeu.edu.cn.
² College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. Dengweiwei@hrbeu.edu.cn.
³ College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. yanyiming@hrbeu.edu.cn.
⁴ College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. xf.yao1020@gmail.com.

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.

Keywords: KRX anomaly detection; hyperspectral imagery; progressive line processing; real-time algorithm; the causal sliding window.