Retrieval of oceanic chlorophyll concentration using support vector machines

Author

Zhan, Haigang ; Shi, Ping ; Chen, Chuqun

Author_Institution

Key Lab. of Tropical Marine Environ. Dynamics, Chinese Acad. of Sci., Guangzhou, China

Volume

41

Issue

12

fYear

2003

Firstpage

2947

Lastpage

2951

Abstract

This letter investigates the possibility of using a new universal approximator-support vector machines (SVMs)-as the nonlinear transfer function between oceanic chlorophyll concentration and marine reflectance. The SeaBAM dataset is used to evaluate the proposed approach. Experimental results show that the SVM performs as well as the optimal multilayer perceptron (MLP) and can be a promising alternative to the conventional MLPs for the retrieval of oceanic chlorophyll concentration from marine reflectance.

Keywords

geochemistry; oceanographic techniques; organic compounds; remote sensing; support vector machines; SVM; SeaBAM dataset; marine reflectance; nonlinear transfer function; oceanic chlorophyll concentration; support vector machines; universal approximator; Backpropagation algorithms; Multilayer perceptrons; Neural networks; Oceans; Optical noise; Reflectivity; Risk management; Support vector machine classification; Support vector machines; Transfer functions;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/TGRS.2003.819870

Filename

1260634