Remote sensing signatures extraction for hydrological resources management applications

The extraction of hydrological characteristics from a particular geographical region through remote sensing (RS) data processing allows the generation of electronic signature maps, which are the basis to create a high-resolution collection atlas processed in time for a particular geographical zone. This can be achieved using a novel tool developed for supervised segmentation and classification of hydrological remote sensing signatures (HRSS) via the combination of both statistical strategies defined as the Weighted Order Statistics (WOS) and the Minimum Distance to Means (MDM) techniques, unifying their particular advantages. This is referred to as the Hydrological Signatures Classification (HSC) method. The extraction of HRSS from real-world high-resolution environmental RS imagery is reported to probe the efficiency of the developed technique in hydrological resources management applications. � 2009 IEEE

Remote sensing signatures extraction for hydrological resources management applications

We study the dynamics of discrete-time regulatory networks on random digraphs. For this we define ensembles of deterministic orbits of random regulatory networks, and introduce some statistical indicators related to the long-term dynamics of the system. We prove that, in a random regulatory network, initial conditions almost certainly converge to a periodic attractor. We study the subnetworks, which we call modules, where the periodic asymptotic oscillations are concentrated. We prove that those modules are dynamically equivalent to independent regulatory networks. " 2008 IOP Publishing Ltd and London Mathematical Society.",,,,,,"10.1088/0951-7715/21/3/009",,,"http://hdl.handle.net/20.500.12104/44157","http://www.scopus.com/inward/record.url?eid=2-s2.0-43049087039&partnerID=40&md5=6ce97f8f36a127508a7f55c9f21cf53e",,,,,,"3",,"Nonlinearity",,"53