4 research outputs found

    Remote Sensing for International Stability and Security - Integrating GMOSS Achievements in GMES

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    The Joint Research Centre of the European Commission hosted a two-day workshop "Remote sensing for international stability and security: integrating GMOSS achievements in GMES". Its aim was to disseminate the scientific and technical achievements of the Global Monitoring for Security and Stability (GMOSS) network of excellence to partners of ongoing and future GMES projects such as RESPOND, LIMES, RISK-EOS,PREVIEW, BOSS4GMES, SAFER, G-MOSAIC. The objectives of this workshop were: ¿ To bring together scientific and technical people from the GMOSS NoE and from thematically related GMES projects. ¿ To discuss and compare alternative technical solutions (e.g. final experimental understanding from GMOSS, operational procedures applied in projects such as RESPOND, pre-operational application procedures foreseen from LIMES, etc.) ¿ To draft a list of technical and scientific challenges relevant in the next future. ¿ To open GMOSS to a wider forum in the JRC This report contains abstracts of the fifteen contributions presented by European researchers. The different presentations addressed pre-processing, feature recognition, change detection and applications which represents also the structure of the report. The second part includes poster abstracts presented during a separate poster session.JRC.G.2-Global security and crisis managemen

    3D space intersection features extraction from Synthetic Aperture Radar images

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    The main purpose of this Thesis is to develop new theoretical models in order to extend the capabilities of SAR images space intersection techniques to generate three dimensional information. Furthermore, the study aims at acquiring new knowledge on SAR image interpretation through the three dimensional comprehension of the scene. The proposed methodologies allow to extend the known radargrammetric applications to vector data generation, exploiting SAR images acquired with every possible geometries. The considered geometries are points, circles, cylinders and lines. The study assesses the estimation accuracy of the features in terms of absolute and relative position and dimensions, analyzing the nowadays operational SAR sensors with a special focus on the national COSMO-SkyMed system. The proposed approach is original as it does not require the direct matching between homologous points of different images, which is a necessary step for the classical radargrammetric techniques; points belonging to the same feature, circular or linear, recognized in different images, are matched through specific models in order to estimate the dimensions and the location of the feature itself. This approach is robust with respect to the variation of the viewing angle of the input images and allows to better exploit archive data, acquired with diverse viewing geometries. The obtained results confirm the validity of the proposed theoretical approach and enable important applicative developments, especially in the Defence domain: (i) introducing original three dimensional measurement tools to support visual image interpretation; (ii) performing an advanced modelling of building counting only on SAR images; (iii) exploiting SAR images as a source for geospatial information and data; (iv) producing geospatial reference information, such as Ground Control Point, without any need for survey on the ground

    The Pyramids of Gizeh Seen by TerraSAR-X – A Prime Example For Unexpected Scattering Mechanisms in SAR

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    Multiple scattering may render synthetic aperture radar (SAR) image interpretation difficult, particularly when it comes to imaging of man-made structures. In medium-resolution SAR images, contributions from different scattering mechanisms can only be distinguished for large objects and under favorable conditions, like bridges over calm water. Since the launches of TerraSAR-X and COSMO-SkyMed, high-resolution SAR imagery is readily available from every spot on the Earth, and multiple scattering image features will be increasingly found in many of these data. Some of the first TerraSAR-X images show the pyramids of Gizeh in a seemingly unexpected geometry. Instead of being geometrically distorted, the near-range faces of the pyramids look like ground projected. This letter explains this image puzzle by a mixture of two scattering effects. The theory is confirmed by an interferometric evaluation. The discussed image artifact can be seen as representative for many other multiple-scattering SAR imaging scenarios in high-resolution images. This letter also demonstrates once again that the interpretation of complex scattering configurations benefits from interferometric information

    Novel neural network-based algorithms for urban classification and change detection from satellite imagery

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    L`attività umana sta cambiando radicalmente l`ecosistema ambientale, unito anche alla rapida espansione demografica dei sistemi urbani. Benche` queste aree rappresentano solo una minima frazione della Terra, il loro impatto sulla richiesta di energia, cibo, acqua e materiali primi, e` enorme. Per cui, una informazione accurata e tempestiva risulta essere essenziale per gli enti di protezione civile in caso, ad esempio, di catastrofi ambientali. Negli ultimi anni il forte sviluppo di sistemi satellitari, sia dal punto di vista della risoluzione spaziale che di quella radiometrica e temporale, ha permesso una sempre piu` accurato monitoraggio della Terra, sia con sistemi ottici che con quelli RADAR. Ad ogni modo, una piu` alta risoluzione (sia spaziale, che spettrale o temporale) presenta tanti vantaggi e miglioramenti quanti svantaggi e limitazioni. In questa tesi sono discussi in dettaglio i diversi aspetti e tecniche per la classificazione e monitoraggio dei cambiamenti di aree urbane, utilizzando sia sistemi ottici che RADAR. Particolare enfasi e` data alla teoria ed all`uso di reti neurali.Human activity dominates the Earth's ecosystems with structural modifications. The rapid population growth over recent decades and the concentration of this population in and around urban areas have significantly impacted the environment. Although urban areas represent a small fraction of the land surface, they affect large areas due to the magnitude of the associated energy, food, water, and raw material demands. Reliable information in populated areas is essential for urban planning and strategic decision making, such as civil protection departments in cases of emergency. Remote sensing is increasingly being used as a timely and cost-effective source of information in a wide number of applications, from environment monitoring to location-aware systems. However, mapping human settlements represents one of the most challenging areas for the remote sensing community due to its high spatial and spectral diversity. From the physical composition point of view, several different materials can be used for the same man-made element (for example, building roofs can be made of clay tiles, metal, asphalt, concrete, plastic, grass or stones). On the other hand, the same material can be used for different purposes (for example, concrete can be found in paved roads or building roofs). Moreover, urban areas are often made up of materials present in the surrounding region, making them indistinguishable from the natural or agricultural areas (examples can be unpaved roads and bare soil, clay tiles and bare soil, or parks and vegetated open spaces) [1]. During the last two decades, significant progress has been made in developing and launching satellites with instruments, in both the optical/infrared and microwave regions of the spectra, well suited for Earth observation with an increasingly finer spatial, spectral and temporal resolution. Fine spatial sensors with metric or sub-metric resolution allow the detection of small-scale objects, such as elements of residential housing, commercial buildings, transportation systems and utilities. Multi-spectral and hyper-spectral remote sensing systems provide additional discriminative features for classes that are spectrally similar, due to their higher spectral resolution. The temporal component, integrated with the spectral and spatial dimensions, provides essential information, for example on vegetation dynamics. Moreover, the delineation of temporal homogeneous patches reduces the effect of local spatial heterogeneity that often masks larger spatial patterns. Nevertheless, higher resolution (spatial, spectral or temporal) imagery comes with limits and challenges that equal the advantages and improvements, and this is valid for both optical and synthetic aperture radar data [2]. This thesis addresses the different aspects of mapping and change detection of human settlements, discussing the main issues related to the use of optical and synthetic aperture radar data. Novel approaches and techniques are proposed and critically discussed to cope with the challenges of urban areas, including data fusion, image information mining, and active learning. The chapters are subdivided into three main parts. Part I addresses the theoretical aspects of neural networks, including their different architectures, design, and training. The proposed neural networks-based algorithms, their applications to classification and change detection problems, and the experimental results are described in Part II and Part III
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