Extração de características e aprendizado não-supervisionados em imagens hiperespectrais

Abstract

Hyperspectral imagery have hundreds of bands and greater subtle differences discrimination capacity in compariosion with multispectral imagery, which benefits precision applications. However, inherent high spectral resolution and high band correlation in these imagery suggests curse of dimensionality occurrence possibility in pattern recognition processes. So, study of the effects of dimensionality reduction is relevant for this kind of image. Additionaly, is relevant to compare the behavior between linear and non linear reduction methods. In this scenario, the purpose of the present work is to analyze how unsupervised feature extraction and its different approaches affect an unsupervised learning task in hyperspectral imagery. In order to conduct such analysis, the algorithms Principal Component Analysis, Isometric Feature Mapping and Locally Linear Embedding were executed in a set of seven images. Clusterings by K-Means and Expectation Maximization algorithms were built under each execution. Performances were measured by Rand, Jaccard, Kappa, Entropy and Purity indexes and compared by Friedman and Nemenyi statistical tests. Hypothesis tests results have shown that, for 70% of the images, feature extraction deployment raised clustering performance significantly and, in 60% of those cases, nonlinear extraction yielded better results than linear