NDLI: Variable subspace model for hyperspectral anomaly detection

Content Provider	Springer Nature Link
Author	Lo, Edisanter
Copyright Year	2011
Abstract	Detection of anomalies in a large area is an important objective in remote sensing using hyperspectral imaging system. Some conventional anomaly detectors for such an application have a user-specified parameter which is the dimension of the clutter subspace. The range of possible values for this parameter is typically large. In this paper, an anomaly detector with a different parameter is proposed. The anomaly detector partials out the effect of the clutter subspace from a pixel by predicting each spectral component of a pixel using a linear combination of the clutter subspace. The dimension of the clutter subspace can vary from one spectral component to another one and is determined by a parameter based on the maximized squared correlations. The Mahalanobis distance of the resulting residual is defined as the anomaly detector. The experimental results are obtained by implementing the anomaly detector as a global anomaly detector in unsupervised mode with background statistics computed from hyperspectral data cubes with wavelengths in the visible and near-infrared range. The results show that the parameter in the anomaly detector has a significantly reduced number of possible values and the values can be determined automatically in an iterative mode.
Starting Page	393
Ending Page	405
Page Count	13
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	16
Issue Number	3
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2011-08-13
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Anomaly detection Hyperspectral imaging Remote sensing Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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