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NGC 7793 P13 is a variable (luminosity range ∼100) ultraluminous X-ray source proposed to host a stellar-mass black hole of less than 15 M⊙ in a binary system with orbital period of 64 d and a 18–23 M⊙ B9Ia companion. Within the EXTraS (Exploring the X-ray Transient and variable Sky) project, we discovered pulsations at a period of ∼0.42 s in two XMM–Newton observations of NGC 7793 P13, during which the source was detected at LX ∼ 2.1 × 1039 and 5 × 1039 erg s−1 (0.3–10 keV band). These findings unambiguously demonstrate that the compact object in NGC 7793 P13 is a neutron star accreting at super-Eddington rates. While standard accretion models face difficulties accounting for the pulsar X-ray luminosity, the presence of a multipolar magnetic field with B ∼ few × 1013 G close to the base of the accretion column appears to be in agreement with the properties of the system. [ABSTRACT FROM AUTHOR]

This work presents a first attempt to define a framework able to face the challenges related to an efficient exploitation of multispectral and hyperspectral sensors in urban areas for mapping applications. The difference in spatial and spectral information in the data sets, the spatial scale of the materials, and the objects involved in different instances of the ‘urban mapping’ problem require semi-automatic approaches to select the most useful features and techniques according to the final mapping application. The framework proposed here is based on a flexible processing chain and is complemented by some guidelines, aiming at helping practitioners to achieve a satisfying result from different kinds of data sets and for different mapping applications. [ABSTRACT FROM AUTHOR]

In this letter, the problem of detecting urban road networks from high-resolution optical/synthetic aperture radar (SAR) images is addressed. To this end, this letter exploits a priori knowledge about road direction distribution in urban areas. In particular, this letter presents an adaptive filtering procedure able to capture the predominant directions of these roads and enhance the extraction results. After road element extraction, to both discard redundant segments and avoid gaps, a special perceptual grouping algorithm is devised, exploiting colinearity as well as proximity concepts. Finally, the road network topology is considered, checking for road intersections and regularizing the overall patterns using these focal points. The proposed procedure was tested on a pair of very high resolution images, one from an optical sensor and one from a SAR sensor. The experiments show an increase in both the completeness and the quality indexes for the extracted road network [ABSTRACT FROM PUBLISHER]

This letter aims at the extraction of roads and road networks from high-resolution synthetic aperture radar data. Classical methods based on line detection do not use all the information available; indeed, in high-resolution data, roads are large enough to be considered as regions and can be characterized also by their statistics. This property can be used in a classification scheme. Therefore, this letter presents a road extraction method which is based on the fusion of classification (statistical information) and line detection (structural information). This fusion is done at the feature level, which helps to improve both the level of likelihood and the number of the extracted roads. The proposed approach is tested with two classification methods and one line extractor. Results on two different datasets are discussed. [ABSTRACT FROM PUBLISHER]

Provides a first assessment of a coregistration technique suitable for multiangle fine spatial resolution synthetic aperture radar (SAR) images. The technique is based on crossroad and road junction extraction and matching and exploits recently introduced road extraction routines for SAR data. These features are matched using relational and geometrical analysis. Results are encouraging and show the possibility to exploit multiangle SAR available from future airborne and satellite missions. [ABSTRACT FROM PUBLISHER]

Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an "urban area" is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, "urban area" extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO) data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR) data prove the usefulness and flexibility of the framework.