Combined Multiscale Convolutional Neural Networks and Superpixels for Building Extraction In Very High-Resolution Images

Abstract

Fine extraction of buildings in very high-resolution (VHR) images plays an important role in urban planning and management. However, the large-variety in appearances and scales makes it challenge to extract buildings with accuracy. Several literatures demonstrate that convolutional neural networks (CNN) is effective in extracting complex buildings, owing to its superiority in high-level features learning. However, traditional CNN always shows poor performance ix extracting multiscale buildings and building boundary, due to its fixed receptive fields and repeated sub-sampling operations, respectively. Therefore, in this paper, we proposed a novel algorithm combining multiscale CNN (MCNN) model and superpixels to meet these two issues. This algorithm firstly designed a MCNN model by constructing a multiscale training samples database and inputs, to produce the preliminary classification building maps. Then, the boundary information provided by superpixels was combined with the CNN classification map using region-based max voting algorithm to produce the final building result. The effectiveness of this algorithm was tested in two well-known VHR datasets. Experimental results demonstrate that our proposed algorithm is outperformed comparison algorithms in extracting complex building in VHR images.