Application of neural networks to waterline extraction in tidal flat from optic satellite images

Joo Hyung Ryu, Joong Sun Won

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

The waterline extraction is one of the most effective satellite remote sensing tools for studying tidal flat environments and its changes, but has not been investigated in detail. A series of field surveys have been carried out to obtain grain size, moisture contents, field spectrometer measurement, and waterline tracking simultaneously with satellite observation. A neural networks algorithm was developed for extracting waterline in tidal flat from satellite-based remote sensing data and applied to the Gomso tidal flat, Korea. Characteristics of tidal flats and spectral reflectance associated with waterline were analyzed first. We have chosen three bands as input data of neural networks: NIR reflects the amount of suspended sediment content at lower tidal flats; SWIR is sensitive to moisture content and is seriously affected by remaining surface water in sedimentary structures; and TIR appears to be the best among the three bands but its low spatial resolution reduces its utility. The neural network method developed here is independent of tidal situations and robust. The neural networks not only distinguish between tidal flats and seawater out of the images, but it also provides continuous outputs that represent mixed compositions of both features. The values of 0.3 - 0.4 were turned out to be waterline in neural networks output. The neural networks output provided the closest to the ground truth and that of the ETM TIR band.

Original languageEnglish
Pages2026-2028
Number of pages3
Publication statusPublished - 2002 Jan 1
Event2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002) - Toronto, Ont., Canada
Duration: 2002 Jun 242002 Jun 28

Other

Other2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002)
CountryCanada
CityToronto, Ont.
Period02/6/2402/6/28

Fingerprint

tidal flat
shoreline
Optics
Satellites
Neural networks
Remote sensing
moisture content
Moisture
remote sensing
Suspended sediments
spectral reflectance
sedimentary structure
Surface waters
Seawater
suspended sediment
field survey
satellite image
Spectrometers
spatial resolution
spectrometer

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Cite this

Ryu, J. H., & Won, J. S. (2002). Application of neural networks to waterline extraction in tidal flat from optic satellite images. 2026-2028. Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada.
Ryu, Joo Hyung ; Won, Joong Sun. / Application of neural networks to waterline extraction in tidal flat from optic satellite images. Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada.3 p.
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Ryu, JH & Won, JS 2002, 'Application of neural networks to waterline extraction in tidal flat from optic satellite images', Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada, 02/6/24 - 02/6/28 pp. 2026-2028.

Application of neural networks to waterline extraction in tidal flat from optic satellite images. / Ryu, Joo Hyung; Won, Joong Sun.

2002. 2026-2028 Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada.

Research output: Contribution to conferencePaper

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Ryu JH, Won JS. Application of neural networks to waterline extraction in tidal flat from optic satellite images. 2002. Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada.