Bayesian Fusion for Multi-Modal Aerial Images

Abstract

This paper presents a fusion method to combine aerial images from a low flying Unmanned Aerial Vehicle (UAV) with images of other spectral bands from sources such as satellites or commercial hyperspectral imagers. The proposed method propagates information from high-resolution images into other low-resolution modalities while allowing the images to have different spectral channels. This means the relationship between the high-resolution and low-resolution channels is expected to be non-deterministic, non-linear and non-stationary. A novel Gaussian Process (GP) framework was developed to define a stochastic prior over the estimated images. Its covariance function is computed to replicate the local structure of the high-resolution image, and allows the model to infer a high-resolution estimate from a low-resolution channel. Results are presented for natural images acquired by a UAV in a farmland mapping application.