Hydrological modeling in ungauged basins using SAR data

In this paper we propose a methodology devoted to exploit high resolution radars for monitoring water bodies in semi-arid countries. The proposed approach is based on appropriate registration, calibration and processing of SAR data, producing information ready to use by end-users. The obtained results were used to (i) estimate a relationship between surface and volume of water stored in reservoirs and (ii) validate a hydrological model that simulates the time evolution of water availability

Multitemporal Level-1β Products:Definitions, Interpretation, and Applications

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1 β products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen's self-organizing maps dealing with classification problems.</p

Feature Extraction From Multitemporal SAR Images Using Selforganizing Map Clustering and Object-Based Image Analysis

We introduce a new architecture for feature extraction from multitemporal synthetic aperture radar (SAR) data. Its the purpose is to combine classic SAR processing and geographical object-based image analysis to provide a robust unsupervised tool for information extraction from time series images. The architecture takes advantage from the characteristics of the recently introduced RGB products of the Level-1 α and Level-1β families, and employs self-organizing map clustering and object-based image analysis. In particular, the input products are clustered using color homogeneity and automatically enriched with a semantic attribute referring to clusters' color, providing a preclassification mask. Then, in the frame of an application-oriented object-based image analysis, opportune layers measuring scattering and geometric properties of candidate objects are evaluated, and an appropriate rule-set is implemented in a fuzzy system to extract the feature of interest. The obtained results have been compared with those given by existing techniques and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semiarid environment.</p

PaTrOnto, an ontology for patents and trademarks

peer reviewedIn this work, we introduce PaTrOnto, a multilingual ontol- ogy designed for legal knowledge extraction in the domain of patents and trademarks. To the best of our knowledge, this is the first attempt to build an ontology which comprehensively covers the domain of patents and trademarks in a multilingual scenario. PaTrOnto is an OWL ontol- ogy with SKOS multilingual lexicalisation, designed to capture the most most important concepts which occur within legal judgments related to patents and trademarks. We release the first version of this ontology in English, Italian and Bulgarian. The relevance of this ontology is that it allows for both reasoning (being written in OWL) and knowledge ex- traction (thanks to the use of some SKOS properties which provide each ontological concept with informations such as synonyms, examples, defi- nitions, normative references). Furthermore, it has been created in close cooperation with legal experts and computer scientists

Urban Area Mapping Using Multitemporal SAR Images in Combination with Self-Organizing Map Clustering and Object-Based Image Analysis

Mapping urban areas from space is a complex task involving the definition of what should be considered as part of an urban agglomerate beyond the built-up features, thus modelling the transition of a city into the surrounding landscape. In this paper, a new technique to map urban areas using multitemporal synthetic aperture radar data is presented. The proposed methodology exploits innovative RGB composites in combination with self-organizing map (SOM) clustering and object-based image analysis. In particular, the clustered product is used to extract a coarse urban area map, which is then refined using object-based processing. In this phase, Delaunay triangulation and the spatial relationship between the identified urban regions are used to model the urban–rural gradient between a city and the surrounding landscape. The technique has been tested in different scenarios representative of structurally different cities in Italy and Germany. The quality of the obtained products is assessed by comparison with the Urban Atlas of the European Environmental Agency, showing good agreement with the adopted reference data despite their different taxonomies