Exploring ELM-based spatial–spectral classification of hyperspectral images

Abstract

Among the different computational intelligence techniques avalaible for hyperspectral data classification, support vector machines (SVMs) have played a dominant role. Recently, a new learning algorithm for single-layer feedforward neural networks called the extreme learning machine (ELM) was proposed. This technique is competitive with SVMs in terms of accuracy, learning speed, and computational scalability. In this article, we propose and evaluate the use of ELM for land-cover classification from hyperspectral images. In addition, we consider two ELM-based techniques integrating spectral and spatial information of the image. The first is a scheme that uses a majority vote approach in order to combine the results of a pixel-wise spectral classification by ELM and a segmentation map obtained by a watershed algorithm. The second introduces spatial information from a small spatial neighbourhood after the classification by ELM. We show the usefulness of spatial–spectral ELM-based classification techniques in hyperspectral imaging. The results are compared to those obtained by similar SVM-based techniques and show improved classification results and much lower execution time. These simple and computationally cheap methods can be combined with others traditionally applied to hyperspectral images.

Acknowledgements

This work was supported in part by the Ministry of Science and Innovation, Government of Spain, cofounded by the FEDER funds of European Union, under contract TIN 2010–17541, and by Xunta de Galicia, Programme for Consolidation of Competitive Research Groups ref. 2010/28. Pablo acknowledges financial support from the Ministry of Science and Innovation, Government of Spain, under an MICINN-FPI grant.