BAYESIAN METHOD FOR SEGMENTATION OF SAR IMAGES IN ROUGH TERRAIN

Radiometric correction is the essential prerequisite to obtain precise and valuable segmentation of remote sensing images, especially when dealing with mountainous regions where the terrain is more likely to be rough. Important applications such as snow cover segmentation have usually to be performed on images of very rough mountainous terrain where this preprocessing step turns out to be especially demanding. The knowledge of the topography of the imaged area through a digital elevation model (DEM) and of the backscatter function for the different terrain cover types are the basis for radiometric correction. Considering SAR images, the huge amount of processing for geographic and geometric calibration and registration that is needed prior to analysis is well established. Nonetheless, even assuming that these calibration and registration steps can been carried out with high precision algorithms, they are still prone to inaccuracies due to the quality of the terrain geometrical description. In the following is presented a model-based method that, exploiting the information contained both in the DEM and in the image, provides improved estimates, in a Bayesian framework, of the terrain itself and of the radiometric characteristics of the land cover

Analytical evaluation of direct bicarbonate measurement with the new gem premier chemstat in hemodialysis patients

GEM Premier ChemSTAT is a whole-blood analyzer designed for providing a rapid basic metabolic panel, inclusive of creatinine and blood urea nitrogen, with the unique characteristic of providing measured bicarbonate (HCO3-) levels. The aim of this work was to evaluate the clinical performance of HCO3- assessment with this analyser in a real-life hemodialysis setting. Imprecision was calculated at different HCO3- levels, along with assay comparison with Gem Premier 4000 analysers. GEM Premier ChemSTAT displayed an imprecision and a bias (in comparison to GEM Premier 4000) for HCO3- of 0.4% and 37.3% at 20.8 mmol/L, 1.2% and 25.6% at 16.4 mmol/L, and 2.1% and 11.6% at 11.5 mmol/L, respectively, using three levels of HCO3- quality control sample ChemSTAT System Evaluator. At direct comparison with the GEM Premier 4000 in the hemodialysis setting, Bland-Altman analysis of HCO3- levels evidenced a bias (µ) of -4.9 (95% CI, -5.2 to -4.7) mmol/L. Such difference was attenuated by recalculating the GEM ChemSTAT expected HCO3- values from pH and pCO2 using the Henderson Hasselbach equation, µ=-0.07 (95%CI, -0.19 to 0.05) mmol/L (p = .24). In conclusion, our results show a remarkable difference between the HCO3- values reported by GEM ChemSTAT or GEM 4000. New reference values for GEM ChemSTAT HCO3- shall hence be defined according to our findings. We suggest that further investigation and a re-evaluation of the reference range should be made before extending the clinical use of this device

Kalman Tracking of GEO Satellite Signal for Opportunistic Rain Rate Estimation

In the NEFOCAST project we aim at estimating rainfall by the opportunistic use of the signal attenuation due to the propagation channel in satellite communications. The estimation is performed by reverse engineering the effects of the various propagation phenomena on the satellite signal. However, the accuracy of the estimation is affected by several factors: in first place the rapid fluctuations in signal amplitude caused by small-scale irregularities in the tropospheric refractive index; secondly, the perturbations of the orbit of GEO satellites, such as the gravitational effects of the moon and the sun, which, even if periodically counteracted by correction maneuvers, nevertheless cause residual orbit inclinations. The problem with all these factors is that they can cause large deviations in the clear-sky measurements that can be misinterpreted as rain events. In this paper we address these problems by employing two Kalman filters designed to track slow and fast changes of the received signal energy, so that the rain events can be reliably estimated

Real-time high resolution rainfall maps from a network of ground-based interactive satellite terminals: the NEFOCAST project

Rainfall estimation and its spatial distribution are key elements for agriculture. Actually, rainfall maps over cultivated areas are needed for efficient water resources management, while prediction and monitoring of severe precipitation events are required for the estimation of possible damages and risks for crops, animals and infrastructures. Spatial and temporal accuracies of rainfall estimates are crucial, especially in case of intense and localized phenomena. Conventional instruments such as rain gauges provide point estimations, but the setup of a dense network requires high installation and maintenance costs. On the other hand, techniques based on satellite remote sensing or weather radars present specific limitations either in terms of data availability, sources of error, cost, or spatial and temporal resolution. A promising alternative is the exploitation of modern telecommunication technologies that, albeit not specifically developed for rainfall estimation, can bring relevant information through the measurement of the attenuation caused by raindrops on broadcast satellite signals. NEFOCAST1 is a research project funded by Tuscany Region (Italy) which implements such an approach based upon a dense population of ground-based Interactive Satellite Terminals (ISTs). The IST employed in the project is an innovative two-way (i.e., transmit/receive) device named Smart Low-Noise Block converter (SmartLNB). Usage of smart LNBs has many advantages in terms of cost and setup and has a great potential for application worldwide including areas where hydro-meteorological networks are not fully developed. In the framework of this project, an experimental network of SmartLNBs has been installed in Tuscany Region in Central Italy and a dedicated platform (NEFOCAST Service Center) has been set up where the data is collected (via ground-to-satellite link), processed and shared with a number of value-added service providers (VASPs). Real-time estimates (with 1 minute update) of rain rates as ‘seen’ by the SmartLNBs are produced through a processing algorithm based on the relationship between the rain rate and the signal attenuation with respect to clear-sky conditions. The real-time point estimates are filtered with a space-time Kalman filter to predict the pattern and evolution of the rainfall field and produce high resolution maps. This work is focused on the simulation of a set of case studies, featuring several storms with different spatial-temporal patterns and intensities. The 3D simulated rainfall fields are used as a virtual reality for the synthetic reconstruction of a set of SmartLNBs measurements over randomly located points. The relevant rainfall maps are then produced by use of the above mentioned Kalman filter approach over the set of synthetic SmartLNBs measurements. Finally a simple linear model of the storm evolution is introduced to test the ability of such algorithm to reconstruct the dynamic of the precipitation system. The impacts of various factors such as SmartLNB density, satellite link geometry, rainfall characteristics (e.g. horizontal/vertical structure, convective/stratiform event), are investigated and the potential for practical applications is eventually discussed