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Proceedings Paper

Semantic segmentation of buildings in high resolution remote sensing images using conditional random fields
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Paper Abstract

This paper proposes a method of building a semantic segmentation method for high-resolution remote sensing images of conditional random fields. Through a large number of actual data operations comparison, U-Net semantic segmentation model is selected as the improved basic model in many deep convolutional neural network models. In order to improve the singularity of the upsampling operation, the U-Net semantic segmentation model is improved as follows: First, the model's crop-copy connection structure is changed to the pyramid pooling layer, and then the multi-scale representation feature image is used, and the multi-scale is used. The resampling of the feature image and the fine bilinear interpolation yield the maximum response at different scales. The improved U-Net model extracts more complete image features. The rough segmentation results are used as the initial input values of the fully connected conditional random fields (CRFs). The global pixel potential energy is inferred through the fully connected graph, and the feature images are refined. Target matching. Finally, the image features are input to the sigmoid classifier for analysis. The results show that the CRF-SUNet model with introduced conditional random field has high segmentation precision, and the boundary of the segmented building is clear, smooth and complete.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143017 (14 February 2020); doi: 10.1117/12.2539313
Show Author Affiliations
Chunsen Zhang, Xi’an Univ. of Science and Technology (China)
Yingwei Ge, Xi’an Univ. of Science and Technology (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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