Accurate Building Detection in VHR Remote Sensing Images using Geometric Saliency
This work addresses building detection for remote sensing applications, but it is incremental as it builds on geometric methods.
The paper tackled building detection in very high resolution remote sensing images by proposing a geometric building index based on geometric saliency, achieving promising performance and impressive generalization on three public datasets.
This paper aims to address the problem of detecting buildings from remote sensing images with very high resolution (VHR). Inspired by the observation that buildings are always more distinguishable in geometries than in texture or spectral, we propose a new geometric building index (GBI) for accurate building detection, which relies on the geometric saliency of building structures. The geometric saliency of buildings is derived from a mid-level geometric representations based on meaningful junctions that can locally describe anisotropic geometrical structures of images. The resulting GBI is measured by integrating the derived geometric saliency of buildings. Experiments on three public datasets demonstrate that the proposed GBI achieves very promising performance, and meanwhile shows impressive generalization capability.