Abstract
Neural networks are widely used as transfer functions in inverse problems in remote sensing. However, this method still suffers from some problems such as the danger of over-fitting and may easily be trapped in a local minimum. This paper investigates the possibility of using a new universal approximator, support vector machine (SVM), as the nonlinear transfer function in inverse problem in ocean color remote sensing. A field data set is used to evaluate the performance of the proposed approach. Experimental results show that the SVM performs as well as the optimal multi-layer 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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Zhan, H. Application of Support Vector Machines in Inverse Problems in Ocean Color Remote Sensing. In: Wang, L. (eds) Support Vector Machines: Theory and Applications. Studies in Fuzziness and Soft Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10984697_18
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DOI: https://doi.org/10.1007/10984697_18
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24388-5
Online ISBN: 978-3-540-32384-6
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