Anomaly detection in hyperspectral data has received a lot of attention for various applications. The aim of anomaly detection is to detect pixels in the hyperspectral datacube whose spectra differ significantly from the background spectra. In anomaly detection no prior knowledge about the target is assumed. Anomaly detection methods in general estimate the spectra of the background (locally or globally) 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, each depending on several parameters. The aim of this paper is to compare the results of different types of anomaly detection when they are applied to scenes with different complexity: urban scenes with different complexity and rural scenes with sub-pixel anomalies. This paper only considers hyperspectral data in the VNIR and SWIR part of the EM spectrum (λ = 0.4-2.5μm).