Study of the influence of pre-processing on local statistics-based anomaly detector results

Anomaly detection in hyperspectral data has received much attention for various applications and is especially important for defense and security applications. Anomaly detection detects pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Most existing methods estimate the spectra of the (local or global) background and then detect anomalies as pixels with a large spectral distance w.r.t. the determined background spectra. Many types of anomaly detectors have been proposed in literature. This paper reports on a sensitivity study that tries to determine an adequate pre-processing chain for anomaly detection in hyperspectral scenes. The study is performed on a set of five hyperspectral datasets and focuses on statistics-based anomaly detectors.