Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms

This thesis presents a new approach to optimize the performance of a dual beam optical scanning system in terms of its scanning combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the scanning tasks need to be segregated and assigned for each scanner head, and path planning where the best combinatorial paths for each scanner are determined in order to minimize the total motion of scanning time. The knowledge acquired by the process is interpreted and mapped into vectors, which are kept in the database and used by the system to guide its reasoning process. Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. The main motivation for this research is to introduce and evaluate an advance new customized GA. Comparison results of different combinatorial operators, and tests with different probability factors are shown. Also, proposed are the new modifications to existing genetic operator called DPPC (Dynamic Pre-Populated Crossover) together with modification of a simple method of representation, called MLR (Multi-Layered Representation). In addition, the performance of the new operators called GA_INSP (GA Inspection Module), DTC (Dynamic Tuning Crossover), and BCS (Bi-Cycle Selection Method) for a better evolutionary approach to the time-based problem has been discussed in the thesis. The simulation results indicate that the algorithm is able to segregate and assign the tasks for each scanning head and also able to find the shortest scanning path for different types of objects coordination. Besides that, the implementation of the new genetic operators helps to converge faster and produce better results. The representation approach has been implemented via a computer program in order to achieve optimized scanning performance. This algorithm has been tested and implemented successfully via a dual beam optical scanning system