| ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR BUILDING EXTRACTION FROM VERY HIGH RESOLUTION REMOTE SENSING IMAGE |
| Paper ID : 1199-SMPR |
| Authors: |
|
Hamidreza Hosseinpoor *1, Farhad Samadzadegan2 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran 2Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Tehran, Iran |
| Abstract: |
| Extracting building information from remotely sensed data is important for a wide range of geographic and environmental applications. 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 proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. 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 benchmark dataset. Compared with other state-of-the-art methods, our proposed method achieves competitive accuracy. |
| Keywords: |
| building extraction, fully convolutional neural networks, attention mechanism, U-Net |
| Status : Conditional Accept (Poster) |
