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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 48-54     DOI: 10.6046/gtzyyg.2020278
Research on building cluster identification based on improved U-Net
WU Yu1(), ZHANG Jun1, LI Yixu2(), HUANG Kangyu1
1. School of Mining, Guizhou University, Guiyang 550025, China
2. College of Agriculture, Guizhou University, Guiyang 550025, China
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Abstract   Aim

ing at tackling the problem that some edge features of buildings are easily blurred or lost in the extraction of buildings with high resolution image by U-Net, this paper proposes an optimized building extraction method, which firstly enhances the edge of buildings with high resolution image and simultaneously improves the partial convolution process of U-Net. Specific process is as follows: Firstly, the domain change recursive filtering method is used to enhance the edge of the building, and the enhanced image is input into U-Net neural network results for training. To make full use of the rich details characteristics of the buildings on the GF-2 images, the authors tried to extract pairs from training images and label patch on the basis of the original U-Net structure and in the process of coding decoding, so as to increase the training data. These patches further strengthened the positive and negative deep learning of high-dimensional feature for buildings, thus successfully realizing building image segmentation. In this paper, the experimental results of the extraction of GF-2 image buildings in Panjin City of Liaoning Province adjacent to Bohai Bay on September 29, 2017 show that the overall classification accuracy of the buildings detected by U-Net is 75.99% for the shaded and unsatisfied area sample data, and the maximum overall classification accuracy of this method can reach 83.12%, which is 7.13 percentage higher than that of the original U-Net network. It is proved that the U-NET model combined with domain change recursive filtering is effective.

Keywords deep learning      domain change recursive filtering      U-Net      edge enhancement      building extraction     
ZTFLH:  TP751  
Corresponding Authors: LI Yixu     E-mail:;
Issue Date: 21 July 2021
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Yixu LI
Kangyu HUANG
Cite this article:   
Yu WU,Jun ZHANG,Yixu LI, et al. Research on building cluster identification based on improved U-Net[J]. Remote Sensing for Land & Resources, 2021, 33(2): 48-54.
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Fig.1  Flow chart of the experiment
Fig.2  Experimental samples
Fig.3  Filtering effect with different parameters
Fig.4  Influence of different parameters on DTRF-Unet output data
δs δr
0.2 0.3 0.4 0.5 0.6 0.7 0.8
30 80.41 81.94 82.65 83.12 82.96 82.50 81.83
45 80.42 81.96 82.63 83.04 82.86 82.37 81.49
60 80.46 81.97 82.60 83.00 82.82 82.25 81.25
Tab.1  Overall classification accuracy of DTRF-Unet under different parameters(%)
Fig.5  Comparison of results before and after improvement
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