Comparative Analysis of Covariance Matrix Estimation for Anomaly Detection in Hyperspectral Images
Year: 2015
Type: article
Abstract: Covariance matrix estimation is fundamental for anomaly detection, especially for the Reed and Xiaoli Yu (RX) detector. Anomaly detection is challenging in hyperspectral images because the data has a high correlation among dimensions, heavy tailed distributions and multiple clusters. This paper comparatively evaluates modern techniques of covariance matrix estimation based on the performance and volume the RX detector. To address the different challenges, experiments were designed to systematically examine the robustness and effectiveness of various estimation techniques. In the experiments, three factors were considered, namely, sample size, outlier size, and modification in the distribution of the sample.
Authors: Santiago Velasco-Forero, Marcus Chen, Alvina Goh, Sze Kim Pang
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1494076862