Stereollax Net: Stereo Parallax-Based Deep Learning Network for Building Height Estimation

Accurate estimation of building heights is crucial for effective urban planning and resource management as it provides essential geometric information about the urban landscape. Many end-to-end deep learning-based networks have been proposed for image-to-height mapping using high-resolution nonoptical and optical remote sensing imagery. In this study, we develop a novel deep-learning architecture that incorporates a stereo parallax-based mathematical formulation for building height estimation. We estimate stereo formulation parameters include differential parallax ( Δ P ) image, average photo-base (b ), and satellite height (h_s ). The final height map is computed by utilizing these parameters in the stereo parallax equation, thus combining closed-form solutions within the learning paradigm. Moreover, to improve the estimation of Δ P , we also introduce a multiscale differential shortcut connection (MSDSC) module. The MSDSC module integrates high-frequency components into lower resolution baseline decoder features while converting them into high-resolution decoder features. To establish the efficacy of our proposed stereo parallax-based deep learning network (Stereollax Net), we train and evaluate our method on densely populated cities of China (42-Cities dataset) and on the IEEE Data Fusion Contest 2018 (DFC2018) dataset. Our proposed Stereollax Net is trained only with RGB imagery and compared with the state-of-the-art (SOTA) methods that utilize both panchromatic and multispectral (RGB and near-infrared) satellite imagery. The qualitative and quantitative results demonstrate that our Stereollax Net surpasses existing SOTA algorithms, achieving superior performance with fewer data and training parameters by a considerable margin. The code will be made publicly available via the GitHub repository.