Target Detection Under Misspecified Models in Hyperspectral Images

Bajorski, Peter

doi:10.1109/jstars.2012.2188095

Cited by 40 publications

(12 citation statements)

References 13 publications

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“…As such, it has gained widespread attention in the fields of geology, the military, the mining industry, and medical imaging, among others [2,3,4]. Anomaly detection, which is one of the most popular branches in hyperspectral image processing, is capable of uncovering many masked targets of interest without a priori spectral knowledge, and as such, it generally conforms to practical conditions, and has gradually been considered very effective and useful in HSI [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Deng

Yan

et al. 2017

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhao

Deng

Yan

et al. 2017

Sensors

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“…It provides the ability to distinguish the differences of ground-object spectra, so it has a wide range of applications in target detection [1]. Based on the availability of the prior information, the target detection algorithms can be divided into unsupervised and supervised ones.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Meng

et al. 2017

Sensors

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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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“…Many target detection algorithms have been proposed and applied to HSI [1,2,3]. Spectral angle mapper (SAM) [4] may be the simplest one without any assumption on data distribution.…”

Section: Introductionmentioning

confidence: 99%

“…The best AUC (%) value of SRD-Pre is 98.61 while the value of SRD-Post is 84.18. Furthermore, when window-size (w out , w in ) is smaller (e.g.,(7,3)), the performance of SRD-Post is much worse.…”

mentioning

confidence: 97%

Combined sparse and collaborative representation for hyperspectral target detection

Zhang

2015

Pattern Recognition

225

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Target Detection Under Misspecified Models in Hyperspectral Images

Cited by 40 publications

References 13 publications

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

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

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

Combined sparse and collaborative representation for hyperspectral target detection

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