A Comparison of Multivariate Outlier Detection Methods For Finding Hyperspectral Anomalies

Smetek, T.E.; Bauer, Kenneth W.

doi:10.5711/morj.13.4.19

Cited by 21 publications

(14 citation statements)

References 26 publications

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“…Numerous techniques are used to find outliers in hyperspectral data. 5 Here we consider two techniques described below, to try to improve upon the standard methods also described below.…”

Section: Image Acquisitionmentioning

confidence: 99%

Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

2016

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The use of aerial hyperspectral imagery for the purpose of remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of the objects under surveillance. For example, a camouflaged vehicle, deliberately designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared spectrum. This work aims to develop improved target detection methods, using a two-stage approach, firstly by development of a physics-based atmospheric correction algorithm to convert radiance into reflectance hyperspectral image data and secondly by use of improved outlier detection techniques. In this paper the use of the Percentage Occupancy Hit or Miss Transform is explored to provide an automated method for target detection in aerial hyperspectral imagery.

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“…Numerous techniques are used to find outliers in hyperspectral data. 5 Here we consider two techniques described below, to try to improve upon the standard methods also described below.…”

Section: Image Acquisitionmentioning

confidence: 99%

Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

2016

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“…This statement is contested in Smetek,5 where the potential ill effects of a small number of anomalies on the estimation of the covariance matrix are detailed. Similarly, Manolakis et al 3 state that:…”

Section: Introductionmentioning

confidence: 99%

Clustering hyperspectral imagery for improved adaptive matched filter performance

Williams¹,

Bauer²,

Friend³

2013

J. Appl. Remote Sens

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Abstract. This paper offers improvements to adaptive matched filter (AMF) performance by addressing correlation and non-homogeneity problems inherent to hyperspectral imagery (HSI). The estimation of the mean vector and covariance matrix of the background should be calculated using "target-free" data. This statement reflects the difficulty that including target data in estimates of the meanvector and covariance matrix of the background could entail. This data could act as statistical outliers and severely contaminate the estimators. This fact serves as the impetus for a 2-stage process: First, attempt to remove the target data from the background by way of the employment of anomaly detectors. Next, with remaining data being relatively "target-free" the way is cleared for signature matching. Relative to the first stage, we were able to test seven different anomaly detectors, some of which are designed specifically to deal with the spatial correlation of HSI data and/or the presence of anomalous pixels in local or global mean and covariance estimators. Relative to the second stage, we investigated the use of cluster analytic methods to boost AMF performance. The research shows that accounting for spatial correlation effects in the detector yields nearly "target-free" data for use in an AMF that is greatly benefitted through the use of cluster analysis methods.

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“…RX and its variants take use of a Manhanlobis distance from background statistics [2]. In spite of their effectiveness, they are proved to be susceptible to the masking and swamping effect, due to the contaminated background statistics [3].…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate outlier detection methods, focusing to alleviate this effect, figure out a more robust metric by eliminating the probable background pixels or a contracting iteration procedure to obtain a new covariance matrix [3,4]. Traditional ways include iterative exclusion algorithm, with each iteration excluding the most anomalous samples until the rest samples unchanged.…”

Section: Introductionmentioning

confidence: 99%

Robust Metric based Anomaly Detection in Kernel Feature Space

Zhang

Huang

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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This thesis analyzes the anomalous measurement metric in high dimension feature space, where it is supposed the Gaussian assumption for state-of-art mahanlanobis algorithms is reasonable. The realization of the detector in high dimension feature space is by kernel trick.Besides, the masking and swamping effect is further inhibited by an iterative approach in the feature space. The proposed robust metric based anomaly detection presents promising performance in hyperspectral remote sensing images: the separability between anomalies and background is enlarged; background statistics is more concentrated, and immune to the

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A Comparison of Multivariate Outlier Detection Methods For Finding Hyperspectral Anomalies

Cited by 21 publications

References 26 publications

Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

Clustering hyperspectral imagery for improved adaptive matched filter performance

Robust Metric based Anomaly Detection in Kernel Feature Space

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