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Proceedings Paper

Semantic segmentation network combined with edge detection for building extraction in remote sensing images
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Paper Abstract

Extracting buildings from remote sensing images is a significant task with many applications such as map drawing, city planning, population estimation, etc. However, traditional methods that rely on artificially designed features struggle to perform well due to the diverse appearance and complicated background. In this paper, we design an end-to-end convolutional neural network that combines semantic segmentation and edge detection for building extraction. In addition, we propose a residual unit combined with spatial pyramid pooling (SPP-RU) to yield representations of different size receptive fields by multi-branch network. We conduct experiments on WHU building dataset, and the experimental results demonstrate the effectiveness of our method in terms of quantitative and qualitative performance compared with state-of-the-art methods.

Paper Details

Date Published: 14 February 2020
PDF: 6 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300D (14 February 2020); doi: 10.1117/12.2538019
Show Author Affiliations
Zhongze Jiang, Huazhong Univ. of Science and Technology (China)
National Key Lab. of Science and Technology on Multi-Spectral Information Processing (China)
Zhong Chen, Huazhong Univ. of Science and Technology (China)
National Key Lab. of Science and Technology on Multi-Spectral Information Processing (China)
Kaixiang Ji, Huazhong Univ. of Science and Technology (China)
National Key Lab. of Science and Technology on Multi-Spectral Information Processing (China)
Jian Yang, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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