Journal
SCIENCE CHINA-INFORMATION SCIENCES
Volume 63, Issue 4, Pages -Publisher
SCIENCE PRESS
DOI: 10.1007/s11432-019-2791-7
Keywords
building segmentation (BS); convolutional neural networks (CNNs); remote sensing; high order pooling; attention
Funding
- National Natural Science Foundation of China [61922029, 61771192]
- National Natural Science Foundation of China for International Cooperation and Exchanges [61520106001]
- Huxiang Young Talents Plan Project of Hunan Province [2019RS2016]
Ask authors/readers for more resources
Recently, building segmentation (BS) has drawn significant attention in remote sensing applications. Convolutional neural networks (CNNs) have become the mainstream analysis approach in this field owing to their powerful representative ability. However, owing to the variation in building appearance, designing an effective CNN architecture for BS still remains a challenging task. Most of CNN-based BS methods mainly focus on deep or wide network architectures, neglecting the correlation among intermediate features. To address this problem, in this paper we propose a hybrid first and second order attention network (HFSA) that explores both the global mean and the inner-product among different channels to adaptively rescale intermediate features. As a result, the HFSA can not only make full use of first order feature statistics, but also incorporate the second order feature statistics, which leads to more representative feature. We conduct a series of comprehensive experiments on three widely used aerial building segmentation data sets and one satellite building segmentation data set. The experimental results show that our newly developed model achieves better segmentation performance over state-of-the-art models in terms of both quantitative and qualitative results.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available