Signal Subspace Identification in Hyperspectral Linear Mixtures
Signal Subspace Identification in Hyperspectral Linear Mixtures , Proc Iberian Conf. on Pattern Recognition and Image Analysis, Estoril, Portugal, Vol. 3523, pp. 207 - 214, June, 2005.
Digital Object Identifier: 10.1007/11492542_26
Hyperspectral applications in remote sensing are often focused on determining the so-called spectral signatures, i.e., the reflectances of materials present in the scene (endmembers) and the corresponding abundance fractions at each pixel in a spatial area of interest. The determination of the number of endmembers in a scene without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper proposes a new mean squared error approach to determine the
signal subspace in hyperspectral imagery. The method rst estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense.