Ocean colors observed by satellite are the measure of the water leaving reflectance of the investigated area, and vary according to the concentration of water’s constituents. The relationship between satellite-derived ocean colors and chlorophyll a concentrations has been studied for several decades [1-3], and several model-based estimation algorithms have been proposed. Analytical models take account of all parameters relating water leaving reflectance with chlorophyll concentration [4]. In empirical approaches remote sensed data is related to the chlorophyll concentration by interpolation techniques applied to a set of training samples. Several neural networks based algorithms have been proposed for the empirical approach [5-10]. In [11] a performance evaluation between several empirical approaches in inversion problems, shown that the use of the support vector machine (SVM) can improve the state of the art neural network solution. In this paper we propose a SVM specialized on Apulian coastal zones showing very encouraging results

A specialized support vector machine for coastal water chlorophyll retrieval from water leaving reflectances

CHIARADIA, Maria Teresa;
2008

Abstract

Ocean colors observed by satellite are the measure of the water leaving reflectance of the investigated area, and vary according to the concentration of water’s constituents. The relationship between satellite-derived ocean colors and chlorophyll a concentrations has been studied for several decades [1-3], and several model-based estimation algorithms have been proposed. Analytical models take account of all parameters relating water leaving reflectance with chlorophyll concentration [4]. In empirical approaches remote sensed data is related to the chlorophyll concentration by interpolation techniques applied to a set of training samples. Several neural networks based algorithms have been proposed for the empirical approach [5-10]. In [11] a performance evaluation between several empirical approaches in inversion problems, shown that the use of the support vector machine (SVM) can improve the state of the art neural network solution. In this paper we propose a SVM specialized on Apulian coastal zones showing very encouraging results
IEEE International Geoscience and Remote Sensing Symposium, 2008 : IGARSS 2008
978-1-4244-2807-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/21171
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