Siamese Unet Network for Waterline Detection and Barrier Shape Change Analysis from Long-Term and Large Numbers of Satellite Imagery

Hsien Kuo Chang, Wei Wei Chen*, Jia Si Jhang, Jin Cheng Liou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Barrier islands are vital dynamic landforms that not only host ecological resources but often protect coastal ecosystems from storm damage. The Waisanding Barrier (WSDB) in Taiwan has suffered from continuous beach erosion in recent decades. In this study, we developed a SiamUnet network compared to three basic DeepUnet networks with different image sizes to effectively detect barrier waterlines from 207 high-resolution satellite images. The evolution of the barrier waterline shape is obtained to present two special morphologic changes at the southern end and the evolution of the entire waterline. The time periods of separation of the southern end from the main WSDB are determined and discussed. We also show that the southern L-shaped end has occurred recently from the end of 2017 until 2021. The length of the L-shaped end gradually decreases during the summer, but gradually increases during the winter. The L-shaped end obviously has a seasonal and jagged change. The attenuation rate of the land area is analyzed as −0.344 km2/year. We also explore two factors that affect the analysis results, which are the number of valid images selected and the deviation threshold from the mean sea level.

Original languageEnglish
Article number9337
JournalSensors
Volume23
Issue number23
DOIs
StatePublished - Dec 2023

Keywords

  • area attenuation
  • barrier change
  • deep-learning network
  • satellite-derived shoreline

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