Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles

• Published in: IEEE geoscience and remote sensing letters Vol. 9; no. 3; pp. 447 - 451
• Main Authors: Licciardi, G., Marpu, P. R., Chanussot, J., Benediktsson, J. A.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); IEEE - Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Algorithms; Classification; Computer Science; Engineering Sciences; extended morphological profiles (EMPs); Feature extraction; Ground truth; Hyperspectral imaging; Neural networks; neural networks (NNs); nonlinear principal component analysis (NLPCA); Nonlinearity; Principal component analysis; Principal components analysis; Remote sensing; Signal and Image processing; Spectral resolution; Studies; Support vector machines; extended morphological profiles (EMPs); neural networks (NNs); nonlinear principal component analysis (NLPCA); Classification
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2011.2172185

Morphological profiles (MPs) have been proposed in recent literature as aiding tools to achieve better results for classification of remotely sensed data. MPs are in general built using features containing most of the information content of the data, such as the components derived from principal component analysis (PCA). Recently, nonlinear PCA (NLPCA), performed by autoassociative neural network, has emerged as a good unsupervised technique to fit the information content of hyperspectral data into few components. The aim of this letter is to investigate the classification accuracies obtained using extended MPs built from the features of NPCA. A comparison of the two approaches has been validated on two different data sets having different spatial and spectral resolutions/coverages, over the same ground truth, and also using two different classification algorithms. The results show that NLPCA permits one to obtain better classification accuracies than using linear PCA.