DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
This work addresses the challenge of enhancing 3D building models for remote sensing applications, though it appears incremental as it builds on existing cGAN methods with a hybrid architecture.
The paper tackled the problem of refining building shapes in digital surface models (DSMs) by fusing stereo DSMs and panchromatic satellite images using a WNet-cGAN, resulting in improved outlines and edges with more rectangular and sharp features.
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.