Open AccessAnalysis of Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Images
Author(s) -
Chinmayee Dora,
Jharna Majumdar
Publication year - 2021
Publication title -
current journal of applied science and technology
Language(s) - English
Resource type - Journals
ISSN - 2457-1024
DOI - 10.9734/cjast/2021/v40i2131468
Subject(s) - mahalanobis distance , hyperspectral imaging , bhattacharyya distance , divergence (linguistics) , anomaly detection , mathematics , pattern recognition (psychology) , false alarm , pixel , receiver operating characteristic , artificial intelligence , algorithm , constant false alarm rate , anomaly (physics) , statistics , computer science , physics , linguistics , philosophy , condensed matter physics
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.