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Generation of Digital Surface Models (DSMs) from stereo satellite (spaceborne) images is classically performed by Ground Control Points (GCPs) which require site visits and precise measurement equipment. However, collection of GCPs is not always possible and such requirement limits the usage of spaceborne imagery. This study aims at developing a fast, fully automatic, GCP-free workflow for DSM generation. The problems caused by GCP-free workflow are overcome using freely-available, low resolution static DSMs (LR-DSM). LR-DSM is registered to the reference satellite image and the registered LR-DSM is used for i) correspondence generation and ii) initial estimate generation for 3-D reconstruction. Novel methods are developed for bias removal for LR-DSM registration and bias equalization for projection functions of satellite imaging. The LR-DSM registration is also shown to be useful for computing the parameters of simple, piecewise empirical projective models. Recent computer vision approaches on stereo correspondence generation and dense depth estimation are tested and adopted for spaceborne DSM generation. The study also presents a complete, fully automatic scheme for GCPfree DSM generation and demonstrates that GCP-free DSM generation is possible and can be performed in much faster time on computers. The resulting DSM can be used in various remote sensing applications including building extraction, disaster monitoring and change detection.Ph.D. - Doctoral Progra

İnsan beyninin elektromanyetik kaynak görüntülemesinde sınır elemanları yönetiminin paralel uygulaması

Human brain functions are based on the electrochemical activity and interaction of the neurons constituting the brain. Some brain diseases are characterized by abnormalities of this activity. Detection of the location and orientation of this electrical activity is called electro-magnetic source imaging (EMSI) and is of signi cant importance since it promises to serve as a powerful tool for neuroscience. Boundary Element Method (BEM) is a method applicable for EMSI on realistic head geometries that generates large systems of linear equations with dense matrices. Generation and solution of these matrix equations are time and memory consuming due to the size of the matrices and high computational complexity of direct methods. This study presents a relatively cheap and e ective solution the this problem and reduces the processing times to clinically acceptable values using parallel cluster of personal computers on a local area network. For this purpose, a cluster of 8 workstations is used. A parallel BEM solver is implemented that distributes the model eciently to the processors. The parallel solver for BEM is developed using the PETSc library. The performance of the iv solver is evaluated in terms of CPU and memory usage for di erent number of processors. For a 15011 node mesh, a speed-up eciency of 97.5% is observed when computing transfer matrices. Individual solutions can be obtained in 520 ms on 8 processors with 94.2% parallellization eciency. It was observed that workstation clusters is a cost e ective tool for solving complex BEM models in clinically acceptable time. E ect of parallelization on inverse problem is also demonstrated by a genetic algorithm and very similar speed-up is obtained.M.S. - Master of Scienc

Parallel implementation of the accelerated BEM approach for EMSI of the human brain

Boundary element method (BEM) is one of the numerical methods which is commonly used to solve the forward problem (FP) of electro-magnetic source imaging with realistic head geometries. Application of BEM generates large systems of linear equations with dense matrices. Generation and solution of these matrix equations are time and memory consuming. This study presents a relatively cheap and effective solution for parallel implementation of the BEM to reduce the processing times to clinically acceptable values. This is achieved using a parallel cluster of personal computers on a local area network. We used eight workstations and implemented a parallel version of the accelerated BEM approach that distributes the computation and the BEM matrix efficiently to the processors. The performance of the solver is evaluated in terms of the CPU operations and memory usage for different number of processors. Once the transfer matrix is computed, for a 12,294 node mesh, a single FP solution takes 676 ms on a single processor and 72 ms on eight processors. It was observed that workstation clusters are cost effective tools for solving the complex BEM models in a clinically acceptable time