A study on the polarimetric properties of various features using SIR-C data

Spaceborne Imaging Radar (SIR-C) data acquired over Gujarat, India in 1994 were processed and analysed using differnet techniques applicable to polarimetric SAR data such as polarization signatures, polarization index, decomposition of the signal and polarization phase difference and limited groundtruth data. It has been observed that multi-frequency polarimetric data enhances the potential of retrieving geo-physical parameters. The polarization signatures are found to vary with the nature of the target. Target decomposition of the returned signal will be useful for the classification of various features. Polarization Phase Difference (PPD) gives good information about the vegetation parameters.     

From 2D TO 3D Through Modelling Based On A Single Image

Three-dimensional modelling from single images remains an interesting topic of investigation in the research community, even though range sensors are becoming a common alternative for the generation of 3D information. The interest in single-image-based modelling is motivated by a wide spectrum of applications such as cultural heritage, civil engineering, urban planning and even criminology. In this paper a complete new production flowline is presented for modelling based on a single image. The modelling process consists of a series of familiar steps in photogrammetry and computer vision: feature extraction, vanishing point computation, camera self-calibration, 3D reconstruction and dimensional analysis. In particular, the methodology developed for single-image-based modelling takes a scientific approach combining several proven techniques with robust estimators. Finally, in order to demonstrate its capabilities, the reported examples include several real situations applied in different contexts.     

Geodetic intersection on the ellipsoid

Through each of two known points on the ellipsoid a geodesic is passing in a known azimuth. We solve the problem of intersection of the two geodesics. The solution for the latitude is obtained as a closed formula for the sphere plus a small correction, of the order of the eccentricity of the ellipsoid, which is determined by numerical integration. The solution is iterative. Once the latitude is obtained, the longitude is determined without iteration.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

GPS signal acquisition using the repeatability of successive code phase measurements

This paper introduces a new method for GPS signal acquisition, which is based on the repeatability of successive code phase measurements and the M-of-N search algorithm. The performance of the proposed method in terms of probability of signal detection is similar to that of traditional methods, except that the calculation of the probability of detection does not rely on the noise distribution or the Carrier-to-Noise ratio (C/N₀). The code phase repeatability-based method is presented along with equations for probability of detection and probability of false detection. If the distribution of the noise is known, it also provides an estimate of the C/N₀. The proposed method is illustrated for coherent and non-coherent acquisition and C/N₀ estimation.     

Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.

Mirdita Zone ophiolites and associated sediments in Albania reveal Neotethys Ocean origin

The Mirdita Ophiolite Zone in Albania is associated with widespread mélanges containing components of up to nappe-size. We dated matrix and components of the mélange by radiolarians, conodonts, and other taxa. The components consist of radiolarites, pelagic limestones and shallow-water limestones, all of Triassic age, as well as ophiolites. Triassic radiolarite as a primary cover of ophiolite material proves Middle Triassic onset of Mirdita ocean-floor formation. The mélange contains a turbiditic radiolarite-rich matrix (“radiolaritic flysch”), dated as Late Bajocian to Early Oxfordian. It formed as a synorogenic sediment during west-directed thrusting of ophiolite and sediment-cover nappes representing ocean floor and underplated fragments of the western continental margin. The tectonic structures formed during these orogenic events (“Younger Kimmeridian or Eohellenic Orogeny”) are sealed by Late Jurassic platform carbonates. The geological history conforms with that of the Inner Dinarides and adjoining areas; we therefore correlate the Mirdita-Pindos Ophiolite Zone with the Vardar Zone and explain its present position by far-distance west-directed thrusting.