Coregistration of Satellite Images and Airborne LiDAR Data Through the Automatic Bias Reduction of RPCs

چکیده مقاله

Rational polynomial coefficients (RPCs), which are provided for most of the commercial satellite images, match relevant image locations to their three-dimensional-ground positions. In spite of their key role in most of remote sensing analysis, a considerable amount of bias is usually seen in the RPCs that necessitate their refinement as a common preprocessing. This refinement is typically performed using some complimentary control information (e.g., ground control points). This paper proposes a matching scheme between the georeferenced airborne LiDAR data and high-resolution satellite images (HRSI) to automatically obtain the control data that is required for RPCs bias compensation. The main contribution of this paper is the design and implementation of a shadow-based matching strategy to correspond the inherently different HRSI and LiDAR data. In this process, RPCs are regarded as the initial relating model between HRSI and LiDAR data and the existing bias of these parameters are ultimately reduced. A novel method, called incremental clustering, is used to automatically detect the shadow cast from the HRSI. In addition, a geometrical method is developed for shadow reconstructions from LiDAR data. These generated shadow maps are then assimilated from geometrical point of view as well as coordinate systems. Finally, a frequency domain matching is performed to find and compensate for the existent bias in RPCs. The obtained results indicate a precise bias reduction of the RPCs, where the initial misalignment of georeferencing is improved from 18 m (30 pixels) to about 0.58 m (1 pixel).