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—In the field of remote sensing, the unmixing of hyperspectral images is usually based on the use of a mixing model. Most existing spectral unmixing methods, used in the reflective range [0.4-2.5 µm], rely on a linear model of endmember reflectances. Nevertheless, such a model supposes the pixels at ground level to be uniformly irradiated and the scene to(More)
In this paper, we first briefly recall the principles of the " TIme-Frequency Ratio Of Mixtures " (TIFROM) approach that we recently proposed. We then show that, unlike Independent Component Analysis (ICA) methods, our approach can separate dependent signals, provided there exist some areas in the time-frequency plane where only one source occurs. We(More)
This letter presents new blind separation methods for moving average (MA) convolutive mixtures of independent MA processes. They consist of time-domain extensions of the FastICA algorithms developed by Hyvarinen and Oja for instantaneous mixtures. They perform a convolutive sphering in order to use parameter-free fast fixed-point algorithms associated with(More)
We proposed recently a new method for separating linear-quadratic mixtures of independent real sources, based on parametric identification of a recurrent separating structure using an ad hoc algorithm. In this paper, we develop a maximum likelihood approach providing an asymptotically efficient estimation of the model parameters. A major advantage of this(More)
Keywords: Independent component analysis Blind source separation Cramé r–Rao lower bound FastICA algorithm Piecewise stationary model a b s t r a c t We address independent component analysis (ICA) of piecewise stationary and non-Gaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The(More)
Context. This work was conducted as part of the SPECPDR program, dedicated to the study of very small particles and astrochemistry, in Photo-Dissociation Regions (PDRs). Aims. We present the analysis of the mid-IR spectro-imagery observations of Ced 201, NCG 7023 East and NorthWest and ρ Ophiuchi West filament. Methods. Using the data from all four modules(More)
This paper concerns blind mixture identification (BMI) and blind source separation (BSS). We consider non-stationary stochastic sources, more specifically sources with slight time-domain sparsity. We first propose a correlation-based BMI/BSS method for Linear-Quadratic mixtures, called LQ-TEMPCORR. We also investigate the applicability of this type of(More)