Application of non-negative ICA for unmixing of Hyperion data

Abstract

Independent Component Analysis (ICA) is based on the linear mixture model of hyperspectral data and has been widely used in various blind source separations. Non-negative ICA considers non negativity constraint in addition to independency of components and the results will therefore be more real because negative signal sources in an image are not physically meaningful. The non-negative Independent Component Analysis (ICA) was used for detection and mapping of endmembers on a Hyperspectral dataset in this study. This paper surveys the capability of non-negative ICA for endmember detection and unmixing of Hyperion data in a geological terrain. In this research, a small part of the Hyperion scene was processed using the geodesic search algorithm to distinguish the Independent Components (ICs) of the image. The dataset was first topographically and atmospherically corrected and then data quality assessment was performed for recognizing the bad bands. The applied method for unmixing was able to detect the major minerals in the study scene and resulted in more accurate outcome in comparison to regular ICA. The non-negative ICA algorithm satisfactory identified three ICs, of which one showed considerable abundances in the region. The determined minerals are also more compatible to the geology and lithological features in the region. It therefore can map alterations and minerals abundances with higher accuracy. The abundances map of detected minerals was generated using spectral angle mapper method that proved as a useful tool for this purpose.