1 September 2008 Analysis of a shallow water environment by multispectral satellite images using a subpixel classification algorithm
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J. of Applied Remote Sensing, 2(1), 023536 (2008). doi:10.1117/1.2988714
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.
Hung Ming Kao, Hsuan Ren, Chao Shing Lee, "Analysis of a shallow water environment by multispectral satellite images using a subpixel classification algorithm," Journal of Applied Remote Sensing 2(1), 023536 (1 September 2008). http://dx.doi.org/10.1117/1.2988714
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KEYWORDS
Near infrared

Satellites

Earth observing sensors

Satellite imaging

Image classification

Ocean optics

Multispectral imaging

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