Self-supervised fully-convolutional neural networks for segmentation in the object-based image analysis workflow - Bruton, Joshua (My University, 2022)
The collection of object-based image analysis literature prescribes a particular workflow for achieving semantic segmentation in small satellite image data sets, relying on non-learned algorithmic segmentation techniques for object-proposal. This research proposes a novel, learned approach to object-proposal in object-based image analysis using fully convolutional neural networks trained with self-supervised learning and synthetic data. Fully-convolutional neural networks are a state-of-the-art approach to segmentation but have limited spatial reasoning and receptive fields, and they also require large amounts of semantically labelled data for end-to-end training. This research shows that using a fully convolutional neural network within object-based image anal-ysis can effectively incorporate learning into the workflow and alleviate concerns regarding neural networks generalising from synthetic data.
A research report submitted to the School of Computer Science and Applied Mathematics, Faculty of Science, University of Witwatersrand, in partial fulfilment of the requirements for the degree Master of Science, 2022