ELM-based spectral–spatial classification of hyperspectral images using extended morphological profiles and composite feature mappings

Abstract

Extreme Learning Machine (ELM) is a supervised learning technique for a class of feedforward neural networks with random weights that has recently been used with success for the classification of hyperspectral images. In this work, we show that the morphological techniques can be integrated in this kind of classifiers using several composite feature mappings which are proposed for ELM. In particular, we present a spectral–spatial ELM-based classifier for hyperspectral remote-sensing images that integrates the information provided by extended morphological profiles. The proposed spectral–spatial classifier allows different weights for both spatial and spectral features, outperforming other ELM-based classifiers in terms of accuracy for land-cover applications. The accuracy classification results are also better than those obtained by equivalent spectral–spatial Support-Vector-Machine-based classifiers.

Additional information

Funding

This work was supported in part by the Ministry of Science and Innovation, Government of Spain, cofounded by the FEDER funds of European Union [grant number TIN2013-41129-P] and by Xunta de Galicia, Programme for Consolidation of Competitive Research Groups [grant number 2014/008].