期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 41, 期 12, 页码 2947-2951出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2003.819870
关键词
oceanic chlorophyll; ocean color remote sensing; neural network; support vector machine (SVM)
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.
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