scholarly journals A Locally Adaptable Iterative RX Detector

2010 ◽  
Vol 2010 (1) ◽  
Author(s):  
Yuri P. Taitano ◽  
Brian A. Geier ◽  
Kenneth W. Bauer
Keyword(s):  
Rx Detector

scholarly journals Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Dirk Borghys ◽  
Ingebjørg Kåsen ◽  
Véronique Achard ◽  
Christiaan Perneel
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Detection Methods ◽  
Multivariate Normal ◽  
Normal Probability ◽  
Local Methods ◽  
Anomaly Detector ◽  
The Difference ◽  
Rx Detector

Anomaly detection (AD) in hyperspectral data has received a lot of attention for various applications. The aim of anomaly detection is to detect pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Many anomaly detectors have been proposed in the literature. They differ in the way the background is characterized and in the method used for determining the difference between the current pixel and the background. The most well-known anomaly detector is the RX detector that calculates the Mahalanobis distance between the pixel under test (PUT) and the background. Global RX characterizes the background of the complete scene by a single multivariate normal probability density function. In many cases, this model is not appropriate for describing the background. For that reason a variety of other anomaly detection methods have been developed. This paper examines three classes of anomaly detectors: subspace methods, local methods, and segmentation-based methods. Representative examples of each class are chosen and applied on a set of hyperspectral data with diverse complexity. The results are evaluated and compared.

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