A Geometric Approach for Hyperspectral and 3D Point Cloud Integration in Natural Scenes

Differently form RGB photos, multi-frame acquisitions of hyperspectral data are unfeasible due to the pushbroom mechanisms of hyperspectral sensors. Therefore, 3D digi-tal surface models including good quality hyperspectral in-formation are available only by integrating 2D images to the point cloud generated using photogrametric cameras or laser scanners. However, geometric differences presented by sen-sor mechanisms resulting in varied perspectives of the same objects present on the scene deeply harm the spatial registration, and thus, the post integration procedure. In this paper we suggest a pre-processing step aiming to adjust objects to the same geometric perspective in order to later integrate the RGB cloud mesh to the hyperspectral data in a more efficient manner. We firstly build the RGB point cloud for later simu-lating the same perspective of push-broom sensor generating a RGB orthomosaic similar to the hyperspectral image. Pre-liminary results indicate an increasing in the number of tie points when registering is performed using the resulting syn-thetic sampling compared to the registration of original data.