

Moser, G, Serpico, SB (2009). Automatic Parameter Optimization for Support Vector Regression for Land and Sea Surface Temperature Estimation From Remote Sensing Data. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 47(3), 909921. Abstract Land surface temperature (LST) and sea surface temperature (SST) are important quantities For many, environmental models. Remote sensing is a source of information for their estimation on both regional and global scales. Many algorithms have been devised to estimate LST and SST from satellite data, most of which require a priori information about the surface and the atmosphere. A recently proposed approach involves the use of support vector machines (SVMs). Based on satellite data and corresponding in situ measurements, they generate an approximation of the relation between them, which can subsequently he used to estimate unknown surface temperatures from additional satellite data. Such a strategy requires the user to set several internal parameters. In this paper, a method is proposed for automatically setting these parameters to quasioptimal values in the sense of minimum estimation errors. This is achieved by minimizing a functional correlated to regression errors (i.e., the spanbound upper bound on the leaveoneout (1,00) error) which can be computed by using only the training set, without need for a further validation set. In order to minimize this functional, Powell's algorithm is adopted, since it is applicable also to nondifferentiable functions. Experimental results yielded by the proposed method are similar in accuracy to those achieved by crossvalidation and by a grid search for the parameter configuration which yields the best testset accuracy. However, the proposed method gives a dramatic reduction in the computational time required, particularly when many training samples are available. DOI: 10.1109/TGRS.2008.2005993 ISSN: 01962892 