True Orthoimage Generation Using Airborne LiDAR Data with Generative Adversarial Network-Based Deep Learning Model

Abstract

Orthoimage, which is geometrically equivalent to a map, is one of the important geospatial products. Displacement and occlusion in optical images are caused by perspective projection, camera tilt, and object relief. A digital surface model (DSM) is essential data for generating true orthoimages to correct displacement and to recover occlusion areas. Light detection and ranging (LiDAR) data collected from an airborne laser scanner (ALS) system is a major source of DSM. The traditional methods require sophisticated procedures to produce a true orthoimage. Most methods utilize 3D coordinates of the DSM and multiview images with overlapping areas for orthorectifying displacement and detecting and recovering occlusion areas. LiDAR point cloud data provides not only 3D coordinates but also intensity information reflected from object surfaces in the georeferenced orthoprojected space. This paper proposes true orthoimage generation based on a generative adversarial network (GAN) deep learning (DL) with the Pix2Pix model using intensity and DSM of the LiDAR data. The major advantage of using LiDAR data is that the data is occlusion-free true orthoimage in terms of projection geometry except in the case of low image quality. Intensive experiments were performed using the benchmark datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The results demonstrate that the proposed approach could have the capability of efficiently generating true orthoimages directly from LiDAR data. However, it is crucial to find appropriate preprocessing to improve the quality of the intensity of the LiDAR data to produce a higher quality of the true orthoimages.