Fusion of HYDICE hyperspectral data with panchromatic imagery for cartographic feature extraction

Abstract

Research at the Digital Mapping Laboratory has focused on the automated analysis of aerial imagery for cartographic feature extraction. However, it has long been the authors' belief that optimal performance in cartographic feature extraction can be obtained only by the combination, or fusion, of feature extraction systems which use differing information sources and processing methods. This paper describes experiments on the pairwise fusion of cartographic feature extraction systems; surface material maps obtained from the classification of hyper-spectral imagery, digital elevation models derived from stereo panchromatic imagery, and three-dimensional (3D) building hypotheses generated from single panchromatic images. Fusion experiments were performed on three test areas and detailed evaluations conducted. The results showed that using surface material or stereo information to focus processing of the building extraction system led to significantly better overall performance and runtimes. Utilizing building hypotheses to refine material classification showed mixed results, due partially to residual registration errors.