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Journal of Applied Remote Sensing

Analysis of a shallow water environment by multispectral satellite images using a subpixel classification algorithm
Author(s): Hung Ming Kao; Hsuan Ren; Chao Shing Lee
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Paper Abstract

Optical satellite images observed in shallow water represent a mixture of information concerning bottom type, water quality, and water depth. In this study, we extracted such information by estimating the abundance using the mixed pixel classification technique. The method is based on the Orthogonal Subspace Projection algorithm which can first eliminate unwanted information, and then match for the information in which we are interested in. Our results indicated that using the subpixel classification approach, information correlated with water depth can be extracted from optical multi-spectral images. To further test this approach, we used satellite images of Itu-Aba Island in the southern part of the South China Sea as a test image and compared with truth depth data from echo sounder sonar system for verification. The results were promising and showed that the information extracted from the satellite image corresponding to bathymetry was highly correlated to the true water depth.

Paper Details

Date Published: 1 September 2008
PDF: 11 pages
J. Appl. Remote Sens. 2(1) 023536 doi: 10.1117/1.2988714
Published in: Journal of Applied Remote Sensing Volume 2, Issue 1
Show Author Affiliations
Hung Ming Kao, National Taiwan Ocean Univ. (Taiwan)
Hsuan Ren, National Central Univ. (Taiwan)
Chao Shing Lee, National Taiwan Ocean Univ. (Taiwan)


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