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Ensemble of Extreme Learning Machines with trained classifier combination and statistical features for hyperspectral data.
- Source :
-
Neurocomputing . Jan2018, Vol. 271, p28-37. 10p. - Publication Year :
- 2018
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 271
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 125546731
- Full Text :
- https://doi.org/10.1016/j.neucom.2016.04.076