Land Surface Temperature Estimation from Passive Satellite Images using Support Vector Machines

In this paper we focus on the algorithm development allowing an improvement of land surface temperature (LST) estimates obtained from passive remote sensing images. An innovative regression algorithm is presented and results are compared with the main classical algorithms for LST estimation: the Split Window techniques. Specifically, a functional approximation scheme based on Support Vector Machines (SVMs) will be adopted and applied to AVHRR data. Particular attention will be devoted to the optimization of the SVM estimator, focusing on the selection of the employed kernel functions (linear, RBF gaussian, and hyperbolic tangent "tanh"). Results, suggesting the effectiveness of the proposed SVM-based approach, are presented and discussed.