Overview of Hierarchical Models for Hyperspectral Image Classification

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 spa-tially 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 at-tractive for remote sensing image analysis. 1