Spatial-spectral endmember extraction from remotely sensed hyperspectral images using the watershed transformation

In this paper, we investigate the use of the watershed transformation for integrating spatial and spectral information in the process of endmember extraction for spectral unmixing of hyperspectral images. The proposed approach is presented as a preprocessing module designed to automatically select a small subset of pixels containing potentially relevant candidates from both spatial and spectral point of view. Dimensionality reduction is required. The idea is to use the morphological watershed transformation to guide the endmember searching process to spatially homogeneous and spectrally “purer” areas. Here the main assumption is that such areas can be located at the local minima of the catchment basins, and far away from watershed lines that define the transition areas between different regions, expected to contain mixed pixels. Experimental results, conducted using a database of 28 simulated hyperspectral data sets obtained through manipulation of a real hyperspectral image acquired by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over a mixed scenario including agricultural, vegetation, and urban areas, suggests a promising trade-off between percentage of endmember candidates retained and degree of spectral purity of predominant endmembers.