Automatic shoreline detection by processing planview timex images using bi-LSTM Networks
A new automatic shoreline detection method by using a bidirectional Long Short-Term Memory (bi-LSTM) Network that processes images column by column is presented. The model is trained on manually extracted shorelines from time-exposure video-images and is very robust against the selection of images f...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/405069 |
| Acceso en línea: | https://hdl.handle.net/2117/405069 https://dx.doi.org/10.1016/j.eswa.2023.122566 |
| Access Level: | acceso abierto |
| Palabra clave: | Coast changes Image processing--Digital techniques Coastal environment Timex video images Automatic shoreline detection Bi-LSTM Neural Network CIELAB colour space Imatges--Processament--Tècniques digitals Costes Àrees temàtiques de la UPC::Enginyeria civil::Geologia |
| Sumario: | A new automatic shoreline detection method by using a bidirectional Long Short-Term Memory (bi-LSTM) Network that processes images column by column is presented. The model is trained on manually extracted shorelines from time-exposure video-images and is very robust against the selection of images for training. Thanks to the novelty of working with image columns, instead of with the whole image, the amount of labelled images for training is limited to a few tens or even less if the conditions are good. Moreover, this column approach makes the model to be robust to variable illuminated images and more easily interpretable, light and fast. There is a wide range of configuration parameters for the bi-LSTM layer by which the system works correctly, which facilitate to use the same network in different video stations. The highest accuracy is obtained by using CIELAB colour space. Without pre-processing the raw colour channels or defining a region of interest and without post-processing the obtained shorelines, the model demonstrates impressive accuracy with mean errors of 2.8 pixels (1.4 meters) in Castelldefels and 1.7 pixels (0.85 meters) in Barcelona.The method could also be effective for satellite shoreline detection by using as input channel the water index of the satellite detection techniques. |
|---|