Detecting changes in desert areas is an important challenge for remote sensing images. This may concern the immediate security (when looking for lost people, for example), but also the measurements of environmental changes. In this paper we will develop a new method, based on a multi-user approach, for automatic classification of SAR images which can be used to detect dunes areas in the desert. This will provide a new approach for detecting changes.

MOTIVATION: The use of DNA microarrays allows the monitoring of the extreme microbial diversity encountered in complex samples like environmental ones as well as that of their functional capacities. However, no probe design software currently available is adapted to easily design efficient and explorative probes for functional gene arrays. RESULTS: We present a new efficient functional microarray probe design algorithm called HiSpOD (High Specific Oligo Design). This uses individual nucleic sequences or consensus sequences produced by multiple alignments to design highly specific probes. Indeed, to bypass crucial problem of cross-hybridizations, probe specificity is assessed by similarity search against a large formatted database dedicated to microbial communities containing about 10 million coding sequences (CDS). For experimental validation, a microarray targeting genes encoding enzymes involved in chlorinated solvent biodegradation was built. The results obtained from a contaminated environmental sample proved the specificity and the sensitivity of probes designed with the HiSpOD program. AVAILABILITY: http://fc.isima.fr/~g2im/hispod/.

This paper is about evaluating the interest between studying directs dual images statistical indices and the use of statistical indices build from two SAR multipolarization images. We show an original way of using this kind of images by using two dimensionned statistical calculation in a Markov random field classification. We present results of our segmentation on urban, forest and mangrove areas. Those results are computed from ENVISAT ASAR data in HH and VV polarizations in a tropical context, near Libreville.

The purpose of this work is to provide new fusion methods adapted to multi sensor SAR use for urban area segmentation. We explain first backscattering of radar signal from urban object and characterize variation due to sensors characteristics. Then, we propose two new data fusion methods suitable for radar images. The first one will use similar response to enhance quality of building backscattering and the second one will use two images to calibrate a statistical scheme based on the differences in backscattering from speckled area to urban area. We will show the results of our work applied on tropical areas.