Convolutional neural network for building extraction from high-resolution remote sensing images
Paper ID : 1132-MVIP2020
Hamidreza Hosseinpoor *, Farhad Samadzadegan
Buildings are one of the most important components of the city, and their extraction from high-resolution remote sensing images is used in a wide range of applications such as urban mapping. Due to the complex structure of high-resolution remote sensing images, automatic extraction of buildings has been a challenge in recent years. In this regard, fully convolutional neural networks (FCNs) have shown successful performance in this task. In this research, a method is proposed to improve the famous UNet network. In classical UNet model high-level rich semantic features are fused with low-level high-resolution features with skip connection for pixel-based segmentation of images. However, the fusion of encoder features with features in corresponding decoder part causes ambiguity in segmentation results because low-level features produce high noise in high-level semantic features. We introduced the embedding feature fusion (EFF) block for enhancing the fusion of low-level with high-level features. For performance evaluation, a publicly available data provided with United States Geological Survey (USGS) high-resolution orthoimagery with the spatial Resolution ranges from 0.15m to 0.3m was used in comparison with several state-of-the-art semantic segmentation model. Experimental results have showed that the proposed architecture improves in extracting complex buildings from high resolution remote sensing images.
building extraction, deep learning, convolutional neural network
Status : Paper Accepted (Oral Presentation)