Anomaly and homogeneous region guided endmember extraction for hyperspectral images

In the cases that the spatial resolution of the hyperspectral data is not sufficient, pixel vectors are expressed in terms of abundances of pure signatures, named as endmembers, with spectral mixture analysis. Most of the endmember extraction methods use only the spectral information, whereas spatial pre-processing methods can increase the performance by directing the endmember extraction process to spatially homogeneous regions. However, this approach results in a failure in detecting anomaly endmembers. In this paper, a two-way approach which provides high performance by directing the endmember extraction process to both anomalies and spatially homogenous regions is proposed.