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    Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection
    (ELSEVIER, 2013) Erener, A.
    Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, and small and large buildings with different rooftops, such as concrete, brick, and metal. Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region. Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantial improvements were observed when the SVM and MLC classifications were developed by the addition of more variables, instead of the use of only four bands. In the evaluation of object based accuracy assessment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they also provide higher rates of false alarms. (C) 2011 Elsevier B.V. All rights reserved.
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    Landslide Susceptibility Assessment: What Are the Effects of Mapping Unit and Mapping Method?
    (Springer, 2012) Erener, A.; Düzgün, H. S. B.
    Landslide susceptibility assessment forms the basis of any hazard mapping, which is one of the essential parts of quantitative risk mapping. For the same study area, different susceptibility maps can be achieved depending on the type of susceptibility mapping methods, mapping unit, and scale. Although there are various methods of obtaining susceptibility maps, the efficiency and performance of each method should be evaluated. In this study the effect of mapping unit and susceptibility mapping method on landslide susceptibility assessment is investigated. When analyzing the effect of susceptibility mapping method, logistic regression (LR) which is widely used in landslide susceptibility mapping and, spatial regression (SR), which have not been used for landslide susceptibility mapping, are selected. The susceptibility maps with logistic and spatial regression models are obtained using two different mapping units namely slope unit-based and grid-based mapping units. The procedure for investigation of effect of mapping unit on different susceptibility mapping methods is applied to Kumluca watershed, in Bartin Province of Western Black Sea Region, Turkey. 18 factor maps are prepared for landslide susceptibility assessment in the study region. Geographic information systems and remote sensing techniques are used to create the landslide factor maps, to obtain susceptibility maps and to compare the results. The relative operating characteristics (ROC) curve is used to compare the predictive abilities of each model and mapping unit and also the accuracy is evaluated depending on the observations made during field surveys. By analyzing the area under the ROC curve for grid-based and slope unit-based mapping units, it can be concluded that SR model provide better predictive performance (0.774 in grids and 0.898 in slope units) as compared to the LR model (0.744 in grids and 0.820 in slope units). This result is also supported by the accuracy analysis. For both mapping units, the SR model provides more accurate result (0.55 for grids and 0.57 for slope units) than the LR model (0.50 for grids and 0.48 for slopes). The main reason for this better performance is that the spatial correlations between the mapping units are incorporated into the model in SR while this fact is not considered in LR model.