Experimental comparison of functional and multivariate spectral-based supervised classification methods in hyperspectral image

ABSTRACT

The aim of this article is to assess and compare several statistical methods for hyperspectral image supervised classification only using the spectral dimension. Since hyperspectral profiles may be viewed either as a random vector or a random curve, we propose to confront various multivariate discriminating procedures with functional alternatives. Eight methods representing three important statistical communities (mixture models, machine learning and functional data analysis) have been applied on three hyperspectral datasets following three protocols studying the influence of size and composition of the learning sample, with or without noised labels. Besides this comparative study, this work proposes a functional extension of multinomial logit model as well as a fast computing adaptation of the nonparametric functional discrimination. As a by-product, this work provides a useful comprehensive bibliography and also supplemental material especially oriented towards practitioners.

Keywords:

• Functional multinomial logit model
• hyperspectral profile
• mixture models
• nonparametric functional discrimination
• random forest
• support vector machines

Classification codes:

• 62-07
• 62H30
• 62H35
• 62P30

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported in part by the French National Spatial Agency (CNES) and the Midi-Pyrénées Region.