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1	Object Detection in Domain Specific Stereo-Analysed Satellite Images Grahn, Fredrik, Nilsson, Kristian January 2019 (has links) Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more conventional with different types of Convolutional Neural Networks. A segmentation network was used as well as a few detection networks and different fusions between these. The Convolutional Neural Network approach proved to be the better of the two in terms of precision and recall but the clustering approach was not far behind. This work was done using a relatively small amount of data which potentially could have impacted the results of the Machine Learning models in a negative way. object detection object classification clustering hierarchical clustering object localisation machine learning ai image localisation image segmentation semantic segmentation remote sensing images satellite images domain knowledge support vector machines svm convolutional neural network cnn fully convolutional network fcn you only look once yolo network fusion Computer Sciences Datavetenskap (datalogi)

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Object Detection in Domain Specific Stereo-Analysed Satellite Images