A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

A probabilistic approach to classify the levee reliability

This work aims to evaluate the reliability of these levee systems, calculating the probability of “failure” of determined levee stretches under different loads, using probabilistic methods that take into account the fragility curves obtained through the Monte Carlo Method. For this study overtopping and piping are considered as failure mechanisms (since these are the most frequent) and the major levee system of the Po River with a primary focus on the section between Piacenza and Cremona, in the lower-middle area of the Padana Plain, is analysed. The novelty of this approach is to check the reliability of individual embankment stretches, not just a single section, while taking into account the variability of the levee system geometry from one stretch to another. This work takes also into consideration, for each levee stretch analysed, a probability distribution of the load variables involved in the definition of the fragility curves, where it is influenced by the differences in the topography and morphology of the riverbed along the sectional depth analysed as it pertains to the levee system in its entirety. A type of classification is proposed, for both failure mechanisms, to give an indication of the reliability of the levee system based of the information obtained by the fragility curve analysis. To accomplish this work, an hydraulic model has been developed where a 500-year flood is modelled to determinate the residual hazard value of failure for each stretch of levee near the corresponding water depth, then comparing the results with the obtained classifications. This work has the additional the aim of acting as an interface between the world of Applied Geology and Environmental Hydraulic Engineering where a strong collaboration is needed between the two professions to resolve and improve the estimation of hydraulic risk.

The arcuate fasciculus: an along-tract analysis with white matter probabilistic tractography

Il cervello umano è composto da una rete complessa, formata da fasci di assoni, che connettono le diverse aree cerebrali. Il fascio arcuato collega l’area imputata alla com- prensione del linguaggio con quella dedicata alla sua produzione. Il fascio arcuato è presente in entrambi gli emisferi cerebrali, anche se spesso è utilizzato prevalente- mente il sinistro. In questa tesi sono state valutate, in un campione di soggetti sani, le differenze tra fascio arcuato destro e sinistro, utilizzando la trattografia, metodica avanzata e non invasiva che permette la ricostruzione della traiettoria delle fibre con immagini RM (Risonanza Magnetica) pesate in diffusione. A questo scopo ho utilizzato un algoritmo probabilistico, che permette la stima di probabilità di connessione della fibra in oggetto con le diverse aree cerebrali, anche nelle sedi di incrocio con fibre di fasci diversi. Grazie all’implementazione di questo metodo, è stato possibile ottenere una ricostruzione accurata del fascio arcuato, an- che nell’emisfero destro dove è spesso critica, tanto da non essere possibile con altri algoritmi trattografici. Parametrizzando poi la geometria del tratto ho diviso il fascio arcuato in venti seg- menti e ho confrontato i parametri delle misure di diffusione, valutate nell’emisfero destro e sinistro. Da queste analisi emerge un’ampia variabilità nella geometria dell’arcuato, sia tra diversi soggetti che diversi emisferi. Nell’emisfero destro l’arcuato incrocia maggiormente fibre appartenenti ad altri fasci. Nell’emisfero sinistro le fibre dell’arcuato sono più compatte e si misura anche una maggiore connettività con altre aree del cervello coinvolte nelle funzioni linguistiche. Nella seconda fase dello studio ho applicato la stessa metodica in due pazienti con lesioni cerebrali, con l’obiettivo di testare il danno del fascio arcuato ipsilaterale alla lesione e stimare se nell’emisfero controlaterale si innescassero meccanismi di plastic- ità strutturale. Questa metodica può essere implementata, in un gruppo di pazienti omogenei, per identificare marcatori RM diagnostici nella fase di pianificazione pre- chirurgica e marcatori RM prognostici di recupero funzionale del linguaggio.