Hyperspectral Image Classification Using Functional Data Analysis
- 22 November 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Cybernetics
- Vol. 44 (9), 1544-1555
- https://doi.org/10.1109/tcyb.2013.2289331
Abstract
The large number of spectral bands acquired by hyperspectral imaging sensors allows us to better distinguish many subtle objects and materials. Unlike other classical hyperspectral image classification methods in the multivariate analysis framework, in this paper, a novel method using functional data analysis (FDA) for accurate classification of hyperspectral images has been proposed. The central idea of FDA is to treat multivariate data as continuous functions. From this perspective, the spectral curve of each pixel in the hyperspectral images is naturally viewed as a function. This can be beneficial for making full use of the abundant spectral information. The relevance between adjacent pixel elements in the hyperspectral images can also be utilized reasonably. Functional principal component analysis is applied to solve the classification problem of these functions. Experimental results on three hyperspectral images show that the proposed method can achieve higher classification accuracies in comparison to some state-of-the-art hyperspectral image classification methods.Keywords
Funding Information
- National Natural Science Foundation of China (61075116, 11371007, 91330118, 61273244)
- Natural Science Foundation of Hubei Province (2010CDA008)
- Multi-Year Research of University of Macau (MYRG205(Y1-L4)-FST11-TYY, MYRG187(Y1-L3))-FST11-TYY)
- Start-Up Research of the University of Macau (SRG010-FST11-TYY)
- Science and Technology Development Fund (FDCT) of Macau (FDCT-100-2012-A3)
This publication has 31 references indexed in Scilit:
- Functional relevance learning in generalized learning vector quantizationNeurocomputing, 2012
- Recent advances in techniques for hyperspectral image processingRemote Sensing of Environment, 2009
- A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysisChemometrics and Intelligent Laboratory Systems, 2008
- Support vector machine for functional data classificationNeurocomputing, 2006
- Functional Classification in Hilbert SpacesIEEE Transactions on Information Theory, 2005
- Representation of functional data in neural networksNeurocomputing, 2005
- Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of cornComputers and Electronics in Agriculture, 2003
- Support vector machines for hyperspectral remote sensing classificationPublished by SPIE-Intl Soc Optical Eng ,1999
- A back-propagation neural network for mineralogical mapping from AVIRIS dataInternational Journal of Remote Sensing, 1999
- On the mean accuracy of statistical pattern recognizersIEEE Transactions on Information Theory, 1968