Anomaly detection using topology

Abstract

In this paper we present a new topology-based algorithm for anomaly detection in dimensionally large datasets. The motivating application is hyperspectral imaging where the dataset can be a collection of ~ 10⁶ points in R^k, representing the reflected (or radiometric) spectra of electromagnetic radiation. The algorithm begins by building a graph whose edges connect close pairs of points. The background points are the points in the largest components of this graph and all other points are designated as anomalies. The anomalies are ranked according to their distance to the background. The algorithm is termed Topological Anomaly Detection (TAD). The algorithm is tested on hyperspectral imagery collected with the HYDICE sensor which contains targets of known reflectance and spatial location. Anomaly maps are created and compared to results from the common anomaly detection algorithm RX. We show that the TAD algorithm performs better than RX by achieving greater separation of the anomalies from the background for this dataset.