PROJECT TITLE :

Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data

ABSTRACT:

Synergistic applications based mostly on integrated hyperspectral and lidar knowledge are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar information is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of knowledge and does not exploit the total potential of each sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne knowledge, based on their respective adopted come intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In further to a tie-point-based coregistration, we have a tendency to introduce a ray-tracing-based back projection of the lidar intensities for space-based cost aggregation. The approach consists of 3 processing steps. 1st could be a coarse automatic tie-point-primarily based boresight alignment. The second step coregisters the hyperspectral information to the lidar intensities. Third may be a parametric coalignment refinement with an space-primarily based value aggregation. This hybrid approach of combining tie-purpose features and space-primarily based value aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities leads to a root-mean-square error of one/three pixel. It indicates that a highly integrated and stringent combination of different coalignment methods results in an improvement of the multisensor coregistration.