Dense feature pyramid fusion deep network for building segmentation in remote sensing image

Qinglin Tian; Yingjun Zhao; Kai Qin; Yao Li; Xuejiao Chen

doi:10.1117/12.2587144

12 March 2021 Dense feature pyramid fusion deep network for building segmentation in remote sensing image

Qinglin Tian, Yingjun Zhao, Kai Qin, Yao Li, Xuejiao Chen

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 1176351 (2021) https://doi.org/10.1117/12.2587144
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

It is difficult to achieve detailed segmentation since the building size varies in high-resolution remote sensing images, especially for small buildings. To address these problems, a dense feature pyramid fusion deep network is proposed in this study. First, we built an encoder-decoder structure, and combine attention mechanism and atrous convolution to improve the feature extraction results in the encoder. Second, the pyramid pooling module is selected to extract the multi-scale features from different levels. Finally, dense feature pyramid is adopted in the decoder to fuse multi-level and multi-scale features to obtain the final segmentation results. Experiments on Inria Aerial Image Labeling Dataset show that our method achieves competitive performance compared with other classical semantic segmentation networks.

Citation Download Citation

Qinglin Tian, Yingjun Zhao, Kai Qin, Yao Li, and Xuejiao Chen "Dense feature pyramid fusion deep network for building segmentation in remote sensing image", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 1176351 (12 March 2021); https://doi.org/10.1117/12.2587144

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available