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.

Highlights

  • Because of abundant spectral information, hyperspectral imagery (HSI) has the potential to discover the subtle differences of ground materials that cannot be visually inspected in a multispectral image [1]

  • HSI datasets, datasets, including including one one synthetic synthetic image image and and two two real real images, images, three groups of are utilized for experiments to demonstrate the performance of synthetic the PLP-KRXD

  • Three groups of HSI datasets, including one image and two real images, are utilized for experiments to demonstrate the performance of the are utilized for experiments to demonstrate the performance of the PLP-KRXD

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Summary

Introduction

Because of abundant spectral information, hyperspectral imagery (HSI) has the potential to discover the subtle differences of ground materials that cannot be visually inspected in a multispectral image [1]. The RXD algorithm merely utilizes the low-order linear statistics of hyperspectral data, leading to inferior detection accuracy in complex and changeable ground distributions To address this issue, kernel-RXD (KRXD), a kernel version of the RXD algorithm, is presented by Kwon and Nasrabadi [13]. KRXD exhaustively mines the high-order correlation between spectral bands via a kernel function It can vastly improve the detection accuracy as compared to the RXD when original data are mixed in a non-linear model, which is always the case. The data kernelization in KRXD produces lots of multiplications and the inversion of matrices, thereby consuming a lot of processing time

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