Hyperspectral imaging enables accurate classification, but also presents 
challenges of high-dimensional data analysis. While pixelwise classification 
methods classify each pixel independently, recent studies have shown 
the advantage of considering the correlations between spatially adjacent 
pixels for accurate image analysis. This paper provides an overview 
of the available hierarchical models for spectral-spatial classification 
of hyperspectral images. The two most recent models are experimentally 
compared on a 102-band ROSIS image of the Center of Pavia, Italy. 
The experimental results demonstrate that classification methods using 
hierarchical models are attractive for remote sensing image analysis.