Stochastic image spectroscopy: a discriminative generative approach to hyperspectral image modelling and classification

Abstract

Abstract This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

Keywords

• Hyperspectral image analysis
• Hyperspectral image classification
• Generative modelling and image formation
• Directed graphical models
• Latent variable modelling
• Approximated inference