Hyperspectral Unmixing Algorithm via Dependent Component Analysis
Hyperspectral Unmixing Algorithm via Dependent Component Analysis, Proc IEEE International Geoscience and Remote Sensing Symp.- IGARSS, Barcelona, Spain, Vol. -, pp. 4033 - 4036, July, 2007.
Digital Object Identifier: 10.1109/IGARSS.2007.4423734
This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding
abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers
signatures weighted by the correspondent abundance
fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method
overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over