While in the continuous case, statistical models of histogram equalization/specification would yield exact results, their discrete counterparts fail. This is due to the fact that the cumulative distribution functions one deals with are not exactly invertible. Otherwise stated, exact histogram specification for discrete images is an ill-posed problem. Invertible cumulative distribution functions are obtained by translating the problem in a K-dimensional space and further inducing a strict ordering among image pixels. The proposed ordering refines the natural one. Experimental results and statistical models of the induced ordering are presented and several applications are discussed: image enhancement, normalization, watermarking, etc.

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International audienceA new generation of space-borne SAR sensors were launched in 2006-2007 with ALOS, TerraSAR-X, COSMO-Sky-Med and RadarSat-2 satellites. The data available in different bands (L, C and X bands), with High Resolution (HR) or multi-polarization modes offer new possibilities to monitor glacier displacement and surface evolution by SAR remote sensing. In this paper, the first results obtained with TerraSAR-X HR SAR image time series acquired over the temperate glaciers of the Chamonix Mont-Blanc test site are presented. This area involves well-known temperate glaciers which have been monitored and instrumented i.e. stakes for annual displacement/ablation, GPS for surface displacement and cavitometer for basal displacement, for more than 50 years. The potential of 11-day repeated X-band HR SAR data for Alpine glacier monitoring is investigated by a combined use of in situ measurements and multi-temporal images. Interpretations of HR images, analysis of interferometric pairs and performance assessments of target/texture tracking methods for glacier motion estimation are presented. The results obtained with four time series covering the Chamonix Mont-Blanc glaciers over one year show that the phase information is rarely preserved after 11 days on such glaciers, whereas the high resolution intensity information allows the main glacier features to be observed and displacement fields on the textured areas to be derived

International audienceAn important aspect of satellite image time series is the simultaneous access to spatial and temporal information. Various tools allow end users to interpret these data without having to browse the whole data set. In this paper, we intend to extract, in an unsupervised way, temporal evolutions at the pixel level and select those covering at least a minimum surface and having a high connectivity measure. To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented. We have developed a frequent sequential pattern extraction method adapted to that spatiotemporal context. A successful application to crop monitoring involving optical data is described. Another application to crustal deformation monitoring using synthetic aperture radar images gives an indication about the generic nature of the proposed approach

The problem of learning fuzzy rule-bases is analyzed from the perspective of finding a favorable balance between the accuracy of the system, the speed required to learn the rules, and finally, the interpretability of the rule-bases obtained. Therefore, we introduce a complete design procedure to learn and then optimize the system rule-base, called the Precise and Fast Fuzzy Modeling approach.Under this paradigm, fuzzy rules are generated from numerical data using a parameterizable greedybased learning method called Selection-Reduction, whose accuracy-speed efficiency is confirmed through empirical results and comparisons with reference methods. Qualitative justification for this method is