U-Net ship detection in satellite optical imagery
| dc.contributor.author | Smith, Benjamin | |
| dc.contributor.supervisor | Coady, Yvonne | |
| dc.date.accessioned | 2020-10-05T23:20:32Z | |
| dc.date.available | 2020-10-05T23:20:32Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020-10-05 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Deep learning ship detection in satellite optical imagery suffers from false positive occurrences with clouds, landmasses, and man-made objects that interfere with correctly classifying ships. A custom U-Net is implemented to challenge this issue and aims to capture more features in order to provide a more accurate class accuracy. This model is trained with two different systematic architectures: single node architecture and a parameter server variant whose workers act as a boosting mechanism. To ex-tend this effort, a refining method of offline hard example mining aims to improve the accuracy of the trained models in both the validation and target datasets however it results in over correction and a decrease in accuracy. The single node architecture results in 92% class accuracy over the validation dataset and 68% over the target dataset. This exceeds class accuracy scores in related works which reached up to 88%. A parameter server variant results in class accuracy of 86% over the validation set and 73% over the target dataset. The custom U-Net is able to achieve acceptable and high class accuracy on a subset of training data keeping training time and cost low in cloud based solutions. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/12176 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | distributed systems | en_US |
| dc.subject | u-net | en_US |
| dc.subject | parameter server | en_US |
| dc.subject | geospatial data | en_US |
| dc.subject | satellite optical imagery | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | object segmentation | en_US |
| dc.subject | ship detection | en_US |
| dc.title | U-Net ship detection in satellite optical imagery | en_US |
| dc.type | Thesis | en_US |