Dependent Component Analysis: A Hyperspectral Unmixing Algorithm

Authors:
José M. Nascimento;José M. Bioucas-Dias
Affiliations:
Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações,;Instituto de Telecomunicações and Instituto Superior Técnico, T.U. Lisbon,
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Year:
2007

Citing 3
Cited 0

Independent factor analysis

Neural Computation
Remote Sensing and Image Interpretation

Remote Sensing and Image Interpretation
Signal subspace identification in hyperspectral linear mixtures

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest.This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data.DECA models the abundance fractions 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. The performance of the method is illustrated using simulated and real data.