Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 507–512, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-507-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 507–512, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-507-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR BUILDING EXTRACTION FROM VERY HIGH RESOLUTION REMOTE SENSING IMAGE

H. R. Hosseinpoor and F. Samadzadegan H. R. Hosseinpoor and F. Samadzadegan
  • School of Surveying and Geospatial Information Engineering, College of Engineering, University of Tehran, Iran

Keywords: Building extraction, Fully convolutional neural networks, Attention mechanism, U-Net

Abstract. Buildings are a major element in the formation of cities and are essential for urban mapping. The precise extraction of buildings from remote sensing data has become a significant topic and has received much attention in recent years. The recently developed convolutional neural networks have shown effective and superior performance to perform well on learning high-level and discriminative features in extracting buildings because of the outstanding feature learning and end-to-end pixel labelling abilities. However, it is difficult to use the features of different levels with a certain degree of importance that is appropriate to deep learning networks. To tackle this problem, a network based on U-Nets and the attention mechanism block was proposed. The network contains an encoder part and a decoder part and a spatial attention module. The special architecture presented in this article enhances the propagation of features and effectively utilizes the features at various levels to reduce errors. The other remarkable thing is that attention module blocks only lead to a minimal increase in model complexity. We effectively demonstrate an improvement of building extraction accuracy on challenging Potsdam and Vaihingen benchmark datasets. The results of this paper show that the proposed architecture improves building extraction in very high resolution remote sensing images compared to previous models.