scholarly journals Improvement of RX Algorithm Performance in Anomaly Detection Applied on Hyperspectral Imaging

2011 ◽  
Vol 14 (AEROSPACE SCIENCES) ◽  
pp. 1-7
Author(s):  
A. El-Rewainy ◽  
E. Farouk
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imaging ◽  
Algorithm Performance

Compressive Hyperspectral Imaging and Anomaly Detection

2013 ◽  
Author(s):  
Pradeep Thiyanarantnam ◽  
Stanley Osher ◽  
Susan Chen ◽  
Wotao Yin ◽  
Kevin Kelly
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imaging

scholarly journals Anomaly detection in server metrics with use of one-sided median algorithm

2017 ◽  
Vol 9 (1) ◽  
pp. 5-22
Author(s):  
Szymon Zacher ◽  
Przemysław Ryba
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Detection Rate ◽  
Short Description ◽  
Algorithm Performance ◽  
Mail Service ◽  
Over Time ◽  
Simple Production ◽  
Good Detection

AbstractIn this paper we consider the problem of anomaly detection over time series metrics data took from one of corporate grade mail service cluster. We propose the algorithm based on one-sided median concept and present some results of experiments showing impact of parameters settings on algorithm performance. In addition we present short description of classes of anomalies discovered in monitored system. Proposed one-sided median based algorithm shows great robustness and good detection rate and can be considered as possible simple production ready solution.

scholarly journals Convolutional neural network target detection in hyperspectral imaging for maritime surveillance

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984299
Author(s):  
Sara Freitas ◽  
Hugo Silva ◽  
José Miguel Almeida ◽  
Eduardo Silva
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Spectral Angle Mapper ◽  
Maritime Surveillance ◽  
Aerial Vehicles ◽  
Spectral Angle

This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications.

Security inspection in ports by anomaly detection using hyperspectral imaging technology

2013 ◽  
Author(s):  
Javier Rivera ◽  
Fernando Valverde ◽  
Manuel Saldaña ◽  
Vidya Manian
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imaging ◽  
Imaging Technology ◽  
Security Inspection

scholarly journals Robust anomaly detection in Hyperspectral Imaging

2014 ◽  
Author(s):  
J. Frontera-Pons ◽  
M. A. Veganzones ◽  
S. Velasco-Forero ◽  
F. Pascal ◽  
J. P. Ovarlez ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imaging

scholarly journals Anomaly detection for replacement model in hyperspectral imaging

2021 ◽  
Vol 185 ◽  
pp. 108079
Author(s):  
Francois Vincent ◽  
Olivier Besson ◽  
Stefania Matteoli
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imaging ◽  
Replacement Model

scholarly journals Analysis of Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Images

2021 ◽  
pp. 25-31
Author(s):  
Chinmayee Dora ◽  
Jharna Majumdar
Keyword(s):  
Anomaly Detection ◽  
Mahalanobis Distance ◽  
Detection Algorithm ◽  
Cumulative Probability ◽  
Algorithm Performance ◽  
Spectral Bands ◽  
Hyper Spectral ◽  
Leibler Divergence ◽  
Significant Difference ◽  
Comparable Performance

Anomaly Detection with Hyper Spectral Image (HSI) refers to finding a significant difference between the background and the anomalous pixels present in the image.  This paper offers a study on the Reed Xiaoli Anomaly (RXA) detection algorithm performance investigated for increasing number of spectral bands from 30, 50, 100 to all the spectral bands present in the HSI. The original RXA algorithm is formulated with Mahalanobis distance. In this study the RXA al is re-implemented with other different distance algorithms like Bhattacharya distance, Kullback-Leibler divergence, and Jeffery divergence and evaluated for any change in the performance. For the first part of investigation, the obtained results showed that the decreased number of spectral bands shows better performance in terms of receiver operating characteristic (ROC) obtained for cumulative probability values and false alarm rate. In the next part of study it is found that, the RXA algorithm with Jeffrey divergence has a comparable performance in ROC to that of the RX algorithm with Mahalanobis distance.

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