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

Zhongze Jiang; Zhong Chen; Kaixiang Ji; Jian Yang

doi:10.1117/12.2538019

14 February 2020 Semantic segmentation network combined with edge detection for building extraction in remote sensing images

Zhongze Jiang, Zhong Chen, Kaixiang Ji, Jian Yang

Author Affiliations +

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300D (2020) https://doi.org/10.1117/12.2538019
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Zhongze Jiang, Zhong Chen, Kaixiang Ji, and Jian Yang "Semantic segmentation network combined with edge detection for building extraction in remote sensing images", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300D (14 February 2020); https://doi.org/10.1117/12.2538019

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