Semantic analysis of EO-1 hyperion hyperspectral data

In this paper we propose a new method for unsupervised data model discovery in hyperspectral data, by adopting the Latent Dirichlet Allocation (LDA) text modeling tool. The proposed method relies on defining a representation of hyperspectral data that allows LDA analysis, by defining a correspondence between hyperspectral profiles and visual words. As such, we can use Latent Dirichlet Allocation as a method for discovery of latent (intrinsic and natural) meaningful grouping of the spectral bands. Based on the model parameters we propose several use scenarios for hyperspectral data analysis, allowing us to identify semantically meaningful structures in the observed scene. Experiments using the proposed method were carried on an EO-1 Hyperion data set imaging an agricultural area in Romania, acquired in June 2009.