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| Research on Classification of Construction Waste Based on UAV Hyperspectral Image |
| XU Long-xin1, 2, 3, 4, SUN Yong-hua2, 3, 4*, WU Wen-huan1, ZOU Kai2, 3, 4, HE Shi-jun2, 3, 4, ZHAO Yuan-ming2, 3, 4, YE Miao2, 3, 4, ZHANG Xiao-han2, 3, 4 |
1. National Key Laboratory of Science and Technology on Remote Sensing Information and Image Analysis，Beijing Research Institute of Uranium Geology，Beijing 100029，China
2. Beijing Laboratory of Water Resources Security，Beijing 100048，China
3. Key Laboratory of 3D Information Acquisition and Application of Ministry of Education，Beijing 100048，China
4. State Key Laboratory of Urban Environmental Process and Digital Simulation，Beijing 100048，China
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Abstract The “siege” of construction waste has become the main problem of urban environmental pollution at this stage, severely restricting the sustainable development of the urban ecological environment. A good classification of construction waste is of great significance to protecting urban water resources, improving the utilization rate of urban land and improving residents’ quality of life. In this paper, the GaiaSky-mini 2 push-broom airborne specular imager (400~1 000 nm) is mounted on the DJ MATRICE M600Pro UAV, and a clean and windless test environment is selected to collect hyperspectral remote sensing images of the study area in real-time. The hyperspectral remote sensing images of the study area were preprocessed by geometric correction, image cropping and radiometric correction; The objects in the study were divided into two categories: background objects, including reed, wormwood, water, shadow, bare soil and asphalt road, and construction waste including white plastic, dust cloth, foundation residue and rubble sand. Based on pixel points, select the regions of interest (ROI) of various features as training samples, extract the spectral information of six background features and four types of construction waste in the study area, and make spectral curves based on different spectral feature differences between features. Select feature bands, calculate statistics through bands and select reasonable thresholds, use decision tree classification to separate background features and identify and extract construction waste in the study area. Target different background features and construction waste types were selected to verify the sample points and evaluate the accuracy of the separation results of background features and the identification results of construction waste.The results show that the overall recognition and classification accuracy of background features and construction waste is 85.91%, and the Kappa coefficient is 0.845. According to the established decision tree for the separation of background features, the classification effect of six background features is good, among which the producer accuracy of reed, asphalt road and bare soil is 95%, and the overall separation of background features is good. According to the established construction waste identification decision tree, the producer accuracy of dust cloth and rubble sand is 95%, and the producer accuracy of white plastic and foundation residue is 90%, which can accurately extract construction waste in the study area. This study shows that decision tree classification is realized in the unmanned aerial vehicle (UAV) hyperspectral remote sensing image recognition and extraction of the construction waste has good classification accuracy. Moreover, to verify the unscrewed aerial vehicle (UAV) hyperspectral remote sensing in the field of construction waste classification to extract the scientific nature and feasibility of construction waste classification recognition for future work has a specific practical significance.
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Received: 2020-08-06
Accepted: 2022-09-15
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Corresponding Authors:
SUN Yong-hua
E-mail: syhua1982@163.com
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[1] LI Qing-hai, SUN Bei(李清海, 孙 蓓). China Building Materials Science & Technology(中国建材科技), 2009, 18(4): 119.
[2] LIAO Xiao-han，XIAO Qing，ZHANG Hao(廖小罕, 肖 青, 张 颢). National Remote Sensing Bulletin(遥感学报), 2019, 23(6): 1046.
[3] HONG Yun-fu，YANG Hai-jun，LI Ying，et al(洪运富, 杨海军, 李 营, 等). Environmental Monitoring in China (中国环境监测)，2015, 31(5): 163.
[4] YANG Long，SUN Zhong-yu，TANG Guang-liang, et al(杨 龙, 孙中宇, 唐光良, 等). Tropical Geography(热带地理), 2016, 36(5): 833.
[5] DONG Xin-yu，LI Jia-guo，CHEN Han-yue，et al(董新宇, 李家国, 陈瀚阅, 等). National Remote Sensing Bulletin(遥感学报), 2019, 23(6): 1269.
[6] LIU Yan-jun，ZHANG Gui，WANG Xiao, et al(刘彦君, 张 贵, 王 潇, 等). Bulletin of Surveying and Mapping(测绘通报), 2020，(4): 34.
[7] Diaz-Varela R A, Zarco-Tejada P J, Angileri V, et al. Journal of Environmental Management, 2014, 134: 117.
[8] ZHAO Dong-e, WU Rui, ZHAO Bao-guo, et al(赵冬娥, 吴 瑞, 赵宝国, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(3): 921.
[9] Zheng Yan，Bai Jiarui，Xu Jingna，et al. Waste Management, 2018, 72: 87.
[10] Hollstein F，Wohllebe M，Arnaiz S，et al. Proc SPIE, 2016, 9862: 98620J.
[11] GE Ming-feng，QI Hong-xing，WANG Yi-kun，et al(葛明锋, 亓洪兴, 王义坤, 等). Infrared and Laser Engineering(红外与激光工程), 2015, 44(11): 3402.
[12] YANG Hong-yan，DU Jian-min，WANG Yuan, et al(杨红艳, 杜建民, 王 圆, 等). Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2019, 50(4): 188.
[13] LIU Yong-hong，NIU Zheng，WANG Chang-yao(刘勇洪, 牛 铮, 王长耀). National Remote Sensing Bulletin(遥感学报), 2005，(4): 405.
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