The exploitation of a multi-temporal stack of SAR intensity images seems to provide satisfactory results in flood detection problems when different spectral signature in presence of inundation are observed. Moreover, the use of interferometric coherence information can further help in the discrimination process. Besides the remote sensing data, additional information can be used to improve flood detection. We propose a data fusion approach, based on Bayesian Networks (BNs) , to analyze an inundation event, involving the Bradano river in the Basilicata region, Italy. Time series of COSMO-SkyMed stripmap SAR images are available over the area. The following random variables have been considered in the BN scheme: F, that is a discrete variable, consisting of two states: flood and no flood; the n-dimensional i variable, obtained by the SAR intensity imagery; the m-dimensional γ variable, obtained by the InSAR coherence imagery; the shortest distance d of each pixel from river course. The proposed BN approach allows to independently evaluate the conditional probabilities P(i|F), P(γ|F) and P(F|d), and then to join them to infer the value P(F = flood|i, γ, d), obtaining the probabilistic flood maps (PFMs). We evaluate these PFMs through comparisons with reference flood maps, obtaining overall accuracies higher than 90%.
The purpose of this paper is to test the effectiveness of a Support Vector Machine (SVM) classifier, with gaussian kernel function, in the automatic detection of small lesions from Magnetic Resonance Images (MRIs) of a patientt affected by multiple sclerosis. The data set consists of Proton Density, T2 (the spin-spin relaxation time) Spin-Echo images and a three-dimensional T1-weighted gradient echo sequence, called Magnetization-Prepared RApid Gradient Echo, that can be generated from contiguous and very thin sections, allowing detection of small lesions typically affected by partial volume effects and intersection gaps in T1 weighted Spin-Echol sequences. In this context of classification, SVM with Gaussian kernel function exhibited a good classification accuracy, higher than accuracies obtained, on the same data set, with a traditional RBF, confirming its high generalization capability and its effectiveness when applied to low-dimensional multi-spectral images.