Water depth estimation from Worldview-2 image with back propagation neural network in coastal area

H. Ren, S. Y. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Bathymetry provides important information for marine environment with various hydrological applications. Traditional acoustic echo-sounding techniques have been developed for several decades which can measure water depth more than hundreds of meters. However, they have their limitation in the shallow area when it is difficult to access by ships. Recent airborne bathymetry LIDAR systems can access coastal area without restriction and can measure water depth up to tens of meters in clear water, but the operational expanse is high. The economic approach for bathymetry estimation in coastal area is optical satellite images. They can survey a large area with single or multiple satellite images and the penetration of visible light in water merely reaches 30 meters. In this study, a three-layer back propagation neural network is proposed to estimate bathymetry with limited number of training samples. The experiments show the mean square errors are less than 5 meters.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7863-7865
Number of pages3
ISBN (Electronic)9781538671504
DOIs
StatePublished - 31 Oct 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/TerritorySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • Bathymetry
  • Coastal area
  • Neural network
  • Worldview-2

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