Supervised classification of thermal infrared hyperspectral images through Bayesian, Markovian, and region-based approaches

Hyperspectral images in the thermal infrared range are attracting increasing attention in the remote sensing field. Nonetheless, the generation of land cover maps using this innovative kind of remote sensing data has been scarcely studied so far. The aim of this article is to experimentally investigate the potential of various supervised classification approaches to land cover mapping from high spatial resolution thermal hyperspectral images. The considered methods include both non-contextual and spatial-contextual classifiers, and encompass methodological approaches based on Bayesian decision theory, Markov random fields, multiscale region-based analysis, and Bayesian feature reduction. Experiments were conducted with a challenging data set associated with a complex urban and vegetated scene. Overall accurate results were achieved by using contextual approaches. The validation suggested the effectiveness of pattern recognition tools in the application to this innovative typology of remote sensing data while also indicating potential improvements through the fusion with physically-based methods.