Title :
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
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;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.819870
