Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 7, Issue 4, Pages 1070-1078Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2014.2304304
Keywords
Classification; generative method; hyperspectral images; low-sized training sets; manifold learning; PerTurbo algorithm; remote sensing; support vector machines (SVM)
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Funding
- French Agence Nationale de la Recherche (ANR) [ANR-13-JS02-0005-01]
- Agence Nationale de la Recherche (ANR) [ANR-13-JS02-0005] Funding Source: Agence Nationale de la Recherche (ANR)
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Hyperspectral data analysis has been given a growing attention due to the scientific challenges it raises and the wide set of applications that can benefit from it. Classification of hyperspectral images has been identified as one of the hottest topics in this context, and has been mainly addressed by discriminative methods such as SVM. In this paper, we argue that generative methods, and especially those based on manifold representation of classes in the hyperspectral space, are relevant alternatives to SVM. To illustrate our point, we focus on the recently published PerTurbo algorithm and benchmark against SVM this generative manifold learning algorithm in the context of hyperspectral image classification. This choice is motivated by the fact that PerTurbo is fitted with numerous interesting properties, such as 1) low sensitivity to dimensionality curse, 2) high accuracy in weakly labelled images classification context (few training samples), 3) straightforward extension to on-line setting, and 4) interpretability for the practitioner. The promising results call for an up-to-date interest toward generative algorithms for hyperspectral image classification.
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