PerTurbo Manifold Learning Algorithm for Weakly Labeled Hyperspectral Image Classification

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.