Shallow seawater depth retrieval based on bottom classification from remote sensing imagery

Abstract

Remote sensing technique, replacing conventional sonar bathymetry technique, has become an effective complementary method of mapping submarine terrain where special conditions make the sonar technique difficult to be carried out. At the same time, as one kind of data set, multispectral remote sensing data has the disadvantage of being influenced by the variable bottom types in shallow seawater, when it is applied in bathymetry. This paper puts forward a new method to extract water depth information from multispectral data, considering the bottom classification and the true water depth accuracy. That is the Principal Component Analysis (PCA) technique based on the bottom classification. By the least square regression with significance, the experiment near Qingdao City has obtained more satisfactory bathymetry accuracy than that of the traditional single-band method, with the mean absolute error about 2.57m.

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Foundation item: Under the auspices of Scientific Foundation Research Project of the Ministry of Science and Technology and Chinese Academy of Surveying and Mapping (No. F0610).

Biography: PANG Lei (1971—), female, a native of Weifang City of Shandong Province, Ph. D. candidate, specialized in remote sensing imagery processing and Synthetic Aperture Radar remote sensing application. E-mail: panglei.mail@163.com

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Pang, L., Zhang, Mb., Zhang, Jx. et al. Shallow seawater depth retrieval based on bottom classification from remote sensing imagery. Chin. Geograph.Sc. 14, 258–262 (2004). https://doi.org/10.1007/s11769-003-0056-x

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Key Words

  • multispectral remote sensing
  • bathymetry model
  • PCA
  • bottom classification

CLC number

  • TP79