![]() ![]() Such a resolution discrepancy between optical aerial/satellite images and elevation data is often the case in real world applications. This network has been trained with low-resolution elevation data and the corresponding high-resolution optical urban photographs. ![]() The second sub-system is a super-resolution deep convolutional network, which performs an enhanced-input associative mapping between input low-resolution and high-resolution images. Further improvement in generalization ability of the network is achieved by using dropout. It is shown in the experiments, that the Top-N cost function offers performance gains in comparison to standard MSE. Assuming that most of the N contour pixels of the GT image are also in the top 2N pixels of the re-construction, this modification balances the two pixel categories and improves the generalization behavior of the CNN model. In this variation, the mean square error (MSE) between the reconstructed output image and the provided ground truth (GT) image of building contours is computed on the 2N image pixels with highest values. Another innovation of this approach is the design of a modified custom loss layer named Top-N. Training is performed using three variations of this urban data set and aims at detecting building contours through a novel super-resolved heteroassociative mapping. It accepts aerial photographs depicting densely populated urban area data as well as their corresponding digital elevation maps (DEM). In particular, the network is based on the state of the art super-resolution model SRCNN (Dong, Loy, He, & Tang, 2015). ![]() ![]() The first is a deep convolutional neural network (CNN) method for the detection of building borders. They were designed with the purpose to provide additional, more reliable, information regarding building contours in a future version of the proposed relaxation system. Two novel sub-systems have also been developed in this thesis. All these multisource and multiresolution data are fused so that probable line segments or edges are extracted that correspond to prominent building boundaries. In this thesis, an iterative relaxation system is developed based on the examination of the local context of each edge according to multiple spatial input sources (optical, elevation, shadow & foliage masks as well as other pre-processed data as elaborated in Chapter 6). Building reconstruction from aerial photographs and other multi-source urban spatial data is a task endeavored using a plethora of automated and semi-automated methods ranging from point processes, classic image processing and laser scanning. ![]()
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