Enhancement of hyperspectral unmixing using continuum removal

In hyperspectral remote sensing, a lot of endmember extraction algorithms have been proposed to deal with mixed-pixel problem. These algorithms are based on linear mixture models and search vertex points in the spectral space. However, practically it is unclear that endmember spectra lie around vertex positions in it. This paper presents a new endmember extraction approach that enhances endmember extraction algorithms. In our method, endmembers are clearly put into vertex positions after feature extraction based on continuum removal. This approach is applied to hyperspectral image (HSI) acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over the Cuprite mining site in Nevada. An experiment shows that continuum removal makes the structure of the data cloud understandable even in low dimensional space, which results in enhancement of the SISAL, one of the endmember extraction algorithms.