Ensemble of Extreme Learning Machines with trained classifier combination and statistical features for hyperspectral data.

Remote sensing and hyperspectral data analysis are areas offering wide range of valuable practical applications. However, they generate massive and complex data that is very difficult to be analyzed by a human being. Therefore, methods for efficient data representation and data mining are of high interest to these fields. In this paper, we introduce a novel pipeline for feature extraction and classification of hyperspectral images. To obtain a compressed representation we propose to extract a set of statistical-based properties from these images. This allows for embedding feature space into fourteen channels, obtaining a significant dimensionality reduction. These features are used as an input for the ensemble learning based on randomized neural networks. We introduce a novel method for forming ensembles of Extreme Learning Machines based on randomized feature subspaces and a trained combiner. It is based on continuous outputs and uses a perceptron-based learning scheme to calculate weights assigned to each classifier and class independently. Extensive experiments carried on a number of benchmarks images prove that using proposed feature extraction and extreme learning ensemble leads to a significant gain in classification accuracy.