DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
Remote Sensing Technology Institute, German Aerospace Center (DLR)（德国航空航天中心（DLR）遥感技术研究所）；Technical University of Munich（慕尼黑工业大学）
英文摘要：
We describe the workflow of a digital surface models (DSMs) refinement algorithm using a hybrid conditional generative adversarial network (cGAN) where the generative part consists of two parallel networks merged at the last stage forming a WNet architecture. The inputs to the so-called WNet-cGAN are stereo DSMs and panchromatic (PAN) half-meter resolution satellite images. Fusing these helps to propagate fine detailed information from a spectral image and complete the missing 3D knowledge from a stereo DSM about building shapes. Besides, it refines the building outlines and edges making them more rectangular and sharp.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
