Building Extraction from Satellite Imagery Based on Footprint Map and Bidirectional Connection Map

ZHANG Lili; ZHANG Jinming; LIU Xiongfei; QIAO Hailang; WANG Hongqi

doi:10.11999/JEIT220201

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1435-1444

ZHANG Lili, ZHANG Jinming, LIU Xiongfei, QIAO Hailang, WANG Hongqi. Building Extraction from Satellite Imagery Based on Footprint Map and Bidirectional Connection Map[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1435-1444. doi: 10.11999/JEIT220201

Citation:

ZHANG Lili, ZHANG Jinming, LIU Xiongfei, QIAO Hailang, WANG Hongqi. Building Extraction from Satellite Imagery Based on Footprint Map and Bidirectional Connection Map[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1435-1444. doi: 10.11999/JEIT220201

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Building Extraction from Satellite Imagery Based on Footprint Map and Bidirectional Connection Map

doi: 10.11999/JEIT220201

ZHANG Lili^{1, 2, 3, 4},
ZHANG Jinming^{1, 4
,
,},
LIU Xiongfei^{1, 4},
QIAO Hailang^{1, 4},
WANG Hongqi^{1, 2, 3, 4}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4.
Key Laboratory of Network Information System Technology, Chinese Academy of Sciences, Beijing 100190, China

Funds: The Youth Innovation Promotion Association Foundation (E03307020D), The Special Funds for Creative Research (2022C61540)

Received Date: 2022-03-01
Accepted Date: 2022-06-22
Rev Recd Date: 2022-06-02

Available Online: 2022-06-28

Publish Date: 2023-04-10

Abstract

Abstract

Most state-of-the-art building extraction from satellite imagery are based on binary segmentation. However, the geographic information has not been considered in these methods, thus, it is difficult to extract building accurately. To consider fully the geographic information on feature extraction, a building extraction convolutional neural network based on footprint map and bidirectional connection is proposed. The proposed method is a multi-branch network, which is designed to predict the footprint and bidirectional connection map, respectively. This paper predicts the footprint heatmap of buildings and uses the Non-Maximum Suppression (NMS) algorithm to obtain the pixel coordinates. Another two branches are used to predict positive connectivity and negative connectivity between footprints. Each pair of nodes is connected according to the bidirectional connectivity map to obtain the final building outline. Experiments on the Buildings2Vec dataset demonstrate that the proposed method outperforms various previous work, which illustrate the superiority in building extraction from satellite imagery.
- Satellite imagery,
- Deep learning,
- Footprint prediction,
- Bidirectional connection map

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References(32)

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