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Accurate and efficient semantic segmentation of buildings in high spatial resolution (HSR) remote sensing images is the basis for applications such as fine urban management, high-precision mapping, land resource utilization investigation, and human settlement suitability evaluation. The current building extraction methods based on deep learning can obtain high-level abstract features of images. However, due to the limitation of convolution kernel size and the vanishing gradient, the extraction of some buildings is inaccurate, and some small-volume buildings are missing as the network deepens. In this regard, we design a horizontally connected residual blocks-based multiscale attention network to achieve high-quality extraction of buildings in HSR remote sensing image. In this network, we subdivide each residual block by channel grouping and feature horizontal connection to consider the difference and saliency of feature information between channels, and then combine the output features with multiscale attention module to consider the contextual semantic relationship of different regions and integrate multilevel local and global information of buildings. A stepwise up-sampling mechanism is designed in the decoding process to finally achieve precise semantic segmentation of buildings. We conduct experiments on two public datasets and compare the proposed method with state-of-the-art semantic segmentation methods. The experiments show that our method could achieve better building extraction results in HSR remote sensing image, which proves the effectiveness of our proposed method.

ieee journal of selected topics in applied earth observations and remote sensing

HCRB-MSAN: Horizontally Connected Residual Blocks-Based Multiscale Attention Network for Semantic Segmentation of Buildings in HSR Remote Sensing Images