Using k-way normalized cuts to integrate LiDAR and hyperspectral imagery for segmentation

The segmentation of hyperspectral images (HSIs) is being used in many fields from target detection to classification. In this paper, we propose a new affinity matrix for the normalized cuts algorithms that takes into account both the hyperspectral and LiDAR data for segmentation. The affinity matrix uses both the spatial-spectral as well as the elevation information; and our results show that the segmentation is much more accurate and can distinguish objects better than a plain normalized-cuts algorithm. We show the improvement gained by adding the LiDAR data onto the hyperspectral data, and discuss the parameters selection strategies.