Bathymetric estimation through principal components analysis using IKONOS-2 data

Ana Teodoro; Hernâni Gonçalves; Joaquim Pais-Barbosa

doi:10.1117/12.864565

22 October 2010 Bathymetric estimation through principal components analysis using IKONOS-2 data

Ana Teodoro, Hernâni Gonçalves, Joaquim Pais-Barbosa

Proceedings Volume 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII; 782419 (2010) https://doi.org/10.1117/12.864565
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

The use of satellite remote sensing images could be a valid alternative to the classical methods of bathymetric measurements for depths less than 30 meters. In this work, several pixels corresponding to different depths are considered to numerically evaluate the relation between the water spectral response and the real depth, in the Douro River Estuary (Porto, Portugal). The main concept relies on principal components analysis, which allows for combining the information of the n available spectral bands from the image into an equal number n of principal components. The dataset is composed by an IKONOS-2 image and bathymetric values. An initial analysis was performed in order to determine the viability of the data for bathymetric study of the Douro River estuary. It was proved that it was not possible to find any direct relationship between the DNs of the IKONOS-2 image and depth values. Therefore, a simple linear regression of the bathymetric values on the IKONOS-2 image principal components was considered. A significant correlation was found between the first principal component and the real depths. In the future, the use of simultaneous data and the use of other statistical models such as decision trees may also provide important contributes to improve this methodology.

Citation Download Citation

Ana Teodoro, Hernâni Gonçalves, and Joaquim Pais-Barbosa "Bathymetric estimation through principal components analysis using IKONOS-2 data", Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 782419 (22 October 2010); https://doi.org/10.1117/12.864565

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KEYWORDS

Principal component analysis

Data modeling

Remote sensing

Reflectivity

Statistical analysis

Mouth

Near infrared

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