Unmixing of Hyperspectral Imagery Based on Probabilistic Outputs of Support Vector Machines

This paper proposes to estimate abundances from hyperspectral image using probability outputs of support vector machines(SVMs),trains a SVM with a gauss kernel function,and trains the parameters of an additional sigmoid function to map the SVM outputs into probabilities.An experiment of real hyperspectral image is conducted to validate the procedure.The abundances estimated by SVM comparison with those of the linear spectral unmixing are also given.The experiment results show that the method can provide effective result of abundance estimation for hyperspectral image.