Hyperspectral target detection is one of the most challenging tasks in remote sensing due to limited spectral information. Many algorithms based on matrix decomposition (MD) are proposed promote separation background and targets, but they suffer from two problems: (1) Targets detected with criterion reconstruction residuals, imbalanced number atoms union dictionary may lead misclassification ta...

Functional magnetic resonance (fMRI) studies intend to answer neuroscience questions by statistically analyzing a set of acquired images. Thereby, the aim is to determine those regions in the brain image in which the signal changes upon stimulus presentation. Although MR data are intrinsically complex valued, most tests are commonly applied to magnitude MR images, because these images have the ...

The ability to detect and identify gaseous effluents is a problem that has been pursued with limited success. It has been shown to be possible using the Invariant algorithm on synthetic hyperspectral scenes with a strong single gas release. That however, is a very specific case and leaves room for further investigation. This study looks at more realistic detection and release scenarios. Our imp...

We introduce estimation and test procedures through divergence optimization for discrete or continuous parametric models. This approach is based on a new dual representation for divergences. We treat point estimation and tests for simple and composite hypotheses, extending maximum likelihood technique. An other view at the maximum likelihood approach, for estimation and test, is given. We prove...

We introduce estimation and test procedures through divergence optimization for discrete or continuous parametric models. This approach is based on a new dual representation for divergences. We treat point estimation and tests for simple and composite hypotheses, extending maximum likelihood technique. An other view at the maximum likelihood approach, for estimation and test, is given. We prove...