DSM extraction using variational methods

Digital surface models (DSM) are crucial for applications such as surface deformation analysis using synthetic aperture radar (SAR) interferometry, automatic target recognition, ortorectification of airborne and satellite images, and generation of digital terrain model (DTM). SAR interferometry, SAR radargrammetry, electro optic (EO) photogrammetry, and LIDAR point clouds are common methods for DSM generation. Each method has different coverage for data obtained from sensor, data acquisition cost, and DSM resolution. In this study, a novel approach is proposed for DSM generation using point cloud data. Proposed method models the DSM generation as an optimization problem where the cost function contains a data fidelity term, inpainting term, and total variation (TV) regularization term. As a result, a noise reduced DSM is generated where fine details are preserved.