Browsing Department of Applied Mathematics by browse.metadata.advisor "Fantaye, Yabebal"
Now showing 1 - 1 of 1
Results Per Page
- ItemApplication of convolutional neural networks to building segmentation in aerial images(Stellenbosch : Stellenbosch University, 2018-12) Olaleye, Kayode Kolawole; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.; Fantaye, YabebalENGLISH ABSTRACT : Aerial image labelling has found relevance in diverse areas including urban management, agriculture, climate, mining, and cartography. As a result, research efforts have been intensified to find fast and accurate algorithms. The current state-of-the-art results in this context have been achieved by deep convolutional neural networks (CNNs). This has been possible because of advances in computing technologies such as fast GPUs and the discovery of optimal architectures. One of the main challenges in using deep CNNs is the need for a large set of ground truth labels during the training phase. Moreover, one has to choose optimal values for the many hyperparameters involved in the model construction to get a good result. In this thesis we focus on building segmentation from aerial images, and study the effect of different hyperparameter values, paying particular attention to the generalisation ability of the resulting models. For all our experiments we use the same architecture and performance metric as the one used in Mnih & Hinton (2012). Our investigation found the following main results: 1) when it comes to the size of CNN filters, small size filters perform as good or even better than large sized filters; 2) the LeakyReLU activation functions lead to a better precision-recall curve than ReLU (Rectified Linear unit) and Tanh activation functions; 3) batch-normalization leads to a slightly poor breakeven point than without batch-normalization - this is contrary to what has been found in other studies with different architectures. In addition, we also investigate how well our models generalise to the task of interpreting contexts that are different from the training sets. Drawing from our findings, we gave recommendations on how to make deep CNN models more robust to variations in aerial images of other continent such as Africa where annotations are either unavailable or in short supply.