Image-based beach state classification using a convolutional neural network and transfer learning
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Price, Timothy | |
| dc.contributor.author | Oerlemans, Stan | |
| dc.date.accessioned | 2022-10-15T00:00:38Z | |
| dc.date.available | 2022-10-15T00:00:38Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/42970 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | Subtidal sandbars exhibit complex patterns that can be classified into distinct equilibrium states. Recognition and classification of these beach states are not trivial and hitherto involved manual classification or pre-defined image features. In this thesis a convolutional neural network is designed and applied for the classification of single- and double-barred beach states from Argus imagery in an automated way. | |
| dc.title | Image-based beach state classification using a convolutional neural network and transfer learning | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Earth Surface and Water | |
| dc.thesis.id | 11276 |
