Integrating anomaly detection to spatial preprocessing for endmember extraction of hyperspectral images

Spectral unmixing is the process of identifying pure spectral signatures, called endmembers, from a hyperspectral data, and then expressing each pixel vector in terms of the fractional abundances of these endmembers. Most of the endmember extraction methods in the literature use only the spectral information, whereas the spatial composition of the data is disregarded. Spatial preprocessing methods, that are motivated by the assumption that endmembers are more likely to be located in homogeneous regions instead of transition areas, can alleviate this drawback and hence increase the performance. However, such a preprocessing approach generally results in a failure of extracting anomalous endmembers which can be of importance for many applications. In this paper, a preprocessing approach that guides the endmember extraction process to homogenous regions while retaining the anomaly points, by combining spatial preprocessing with anomaly detection, is proposed.