CONSTRUCTION AND OPTIMIZATION OF SAMPLE SET OF CONSTRUCTION WASTE DUMP

LI Si-qi; LIU Yang; DU Ming-yi; ZHANG Min; XIN Chao; YAO Yi; MA Teng-yue

doi:10.13205/j.hjgc.202003007

Volume 38 Issue 3

Jun. 2020

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LI Si-qi, LIU Yang, DU Ming-yi, ZHANG Min, XIN Chao, YAO Yi, MA Teng-yue. CONSTRUCTION AND OPTIMIZATION OF SAMPLE SET OF CONSTRUCTION WASTE DUMP[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(3): 39-45,8. doi: 10.13205/j.hjgc.202003007

Citation:

LI Si-qi, LIU Yang, DU Ming-yi, ZHANG Min, XIN Chao, YAO Yi, MA Teng-yue. CONSTRUCTION AND OPTIMIZATION OF SAMPLE SET OF CONSTRUCTION WASTE DUMP[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(3): 39-45,8. doi: 10.13205/j.hjgc.202003007

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CONSTRUCTION AND OPTIMIZATION OF SAMPLE SET OF CONSTRUCTION WASTE DUMP

doi: 10.13205/j.hjgc.202003007

1. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China;
2. Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received Date: 2019-11-07

Abstract

Abstract

The unregulated stacking of urban construction waste endangered the environment and the safety of citizens. In order to identify the stacking points of construction waste, aiming at the problem that there was no stacking point sample set for construction waste, this paper used the pixel based remote sensing classification method to build a sample set. On this basis, the adaptive histogram averaging, CS-LBP operator constraints and migration learning were combined to optimize the Wasserstein generative adversarial networks(WGAN) and generate samples to expand the sample set. The experimental results showed that the pixel based remote sensing classification method improved the efficiency of the artificial sample set, and the WGAN optimized method could effectively inherit the color and texture features of the original data to meet the needs of expanding the sample set.
- construction waste dump,
- sample set construction,
- WGAN,
- data enhancement,
- CS-LBP operator

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References(15)

References

杨壮. 面向Bin Picking的虚拟样本集构建及智能识别方法的研究[D].上海:华东理工大学,2019.

鄢文苗,任东,黄应平,等.基于SVM土壤重金属污染评价的训练样本集构建[J].武汉大学学报(理学版),2019,65(3):316-322.

陈泓佑,和红杰,陈帆,等.基于子样本集构建的DCGANs训练方法[J/OL].自动化学报:1-10[2019-10-09

].https://doi.org/10.16383/j.aas.c180677.

赵树阳, 李建武. 基于生成对抗网络的低秩图像生成方法[J]. 自动化学报, 2018, 44(5):829-839.

牛斌,吴鹏,马利,等.一种基于生成对抗网络的行为样本集扩展方法[J].计算机技术与发展,2019(7):43-48.

SWAIN M J, BALLARD D H. Color indexing[J]. International Journal of Computer Vision, 1991, 7(1): 11-32.

庄福振, 罗平, 何清, 等. 迁移学习研究进展[J].软件学报,2015, 26(1): 26-39.

JIANG J, ZHAI C X. A two-stage approach to domain adaptation for statistical classifiers[C]//The 16th ACM Conf. on Information and Knowledge Management. New York: ACM Press, 2007: 401-410.

FANG M, YIN J, ZHU X Q. Transfer learning across networks for collective classification[C]//The 2013 IEEE 13th Int’l Conf. on Data Mining, 2013: 161-170.

DAI W Y, XUE G R, YANG Q, et al. Co-Clustering based classification for out-of-domain documents[C]//The 13th ACM Int’l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2007: 210-219.

DAI W Y, JIN O, XUE G R, et al. Eigentransfer: a unified framework for transfer learning[C]//The 24th Int’l Conf. on Machine Learning. San Francisco: Morgan Kaufmann Publishers, 2009: 193-200.

YANG Y, NEWSAM S. Bag-of-visual-words and spatial extensions for land-use classification[C]//The 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose,USA, 2010.

GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 2014, 3:2672-2680.

ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein GAN[EB/OL]. https://arxiv.org/pdf/1701.07875.pdf. 2018-02-23.

HEIKKIL M, PIETIK I, NEN M, et al. Description of Interest Regions with Local Binary Patterns[J].Pattern Recognition,2009,42(3):425-436.

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