Evaluation of Different Regularization Methods for the Extreme Learning Machine Applied to Hyperspectral Images

During recent years, many regularization techniques have been proposed to deal with ill-posed problems related to hyperspectral image classification, in which the limited number of training samples contrasts with the very high spectral dimensionality. However, the intrinsic structure of a hyperspectral image often depends on the specific scene and spectrometer, although regularizers like Ridge, LASSO, etc, have been widely used in practical applications. Instead of imposing these regularizers to the probabilistic output of a classifier, this work evaluates the use of extreme learning machines (ELM) with output weights of a single-hidden layer feed-forward neural network (SLFN) regularized with Ridge and LASSO priors, respectively. Experimental results with several real hyperspectral images are conducted to compare the performance and adaptation of these two regularizers with the the original ELM in classification scenarios.