ELM-based Spectral-Spatial Classification of Hyperspectral Images using Extended Morphological Profiles and Composite Feature Mappings

TítuloELM-based Spectral-Spatial Classification of Hyperspectral Images using Extended Morphological Profiles and Composite Feature Mappings
AutoresFrancisco Argüello, and Dora B. Heras
TipoArtículo de revista
Fonte International Journal of Remote Sensing, TAYLOR & FRANCIS LTD, Vol. 36, No. 2, pp. 645-664 , 2015.
RankRanked Q1 in Earth and Planetary Sciences (all) by SJR
ISSN0143-1161
DOI10.1080/01431161.2014.999882
AbstractExtreme 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.
Palabras chaveHyperspectral image, spectral-spatial classification, extreme learning machine, extended morphological profile, remote sensing