Instance Segmentation of Buildings Using Keypoints

Qingyu Li, Lichao Mou, Yuansheng Hua, Yao Sun, Pu Jin, Yilei Shi, Xiao Xiang Zhu

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

17 Zitate (Scopus)


Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details.

Titel2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9781728163741
PublikationsstatusVeröffentlicht - 26 Sept. 2020
Extern publiziertJa
Veranstaltung2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, USA/Vereinigte Staaten
Dauer: 26 Sept. 20202 Okt. 2020


NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


Konferenz2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Land/GebietUSA/Vereinigte Staaten
OrtVirtual, Waikoloa


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