| 基于迁移学习再训练模型和高分遥感数据的建筑垃圾自动识别方法 |
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| 引用本文： | 祝一诺,高婷,王术东,周磊,杜明义. 基于迁移学习再训练模型和高分遥感数据的建筑垃圾自动识别方法[J]. 遥感技术与应用, 2021, 36(2): 314-323. DOI: 10.11873/j.issn.1004-0323.2021.2.0314 |
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| 作者姓名： | 祝一诺 高婷 王术东 周磊 杜明义 |
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| 作者单位： | 1.北京建筑大学 测绘与城市空间信息学院，北京 102616;2.北京建筑大学 北京未来城市设计高精尖创新中心，北京 100044 |
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| 基金项目： | 国家重点研发计划课题(2018YFC0706003);北京市教委科技计划项目(KM201810016014) |
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| 摘 要： | 目前城市建筑垃圾大量持续产生且堆积严重,利用率较低同时危害城市生态环境.建筑垃圾的识别是实现建筑垃圾分割、提取以及监测的技术基础,但由于建筑垃圾本身的复杂特征和遥感影像的尺度差异、光谱差异等因素导致其识别和监管困难.提出了 一种利用迁移学习再训练模型来实现自动识别建筑垃圾的方法.首先根据建筑垃圾的典型遥感特征构建样本库...
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| 关 键 词： | 高分遥感影像 建筑垃圾 迁移学习 自动识别 Inception-V3 |
| 收稿时间： | 2019-09-27 |
Automatic Recognition Method of Construction Waste based on Transfer Learning and Retraining Model and High-score Remote Sensing Data |
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| Yinuo Zhu,Ting Gao,Shudong Wang,Lei Zhou,Mingyi Du. Automatic Recognition Method of Construction Waste based on Transfer Learning and Retraining Model and High-score Remote Sensing Data[J]. Remote Sensing Technology and Application, 2021, 36(2): 314-323. DOI: 10.11873/j.issn.1004-0323.2021.2.0314 |
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| Authors: | Yinuo Zhu Ting Gao Shudong Wang Lei Zhou Mingyi Du |
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| Abstract: | At present， a quantity of urban construction waste is constantly produced and seriously accumulated， and its utilization rate is low， which endanger the urban ecological environment. The recognition of construction waste is the technical basis for the segmentation， extraction and monitoring of construction waste. However， it is difficult to identify and monitor construction waste due to its complex characteristics， the scale difference and spectral difference of remote sensing image. In this paper， a method of automatic identification of construction waste based on transfer learning and retraining model is proposed. Firstly， a sample bank is constructed according to the typical remote sensing features of construction waste. Then， based on the advanced international deep learning environment Tensorflow， the Inception-V3 model is retrained by using transfer learning， and the recognition model of construction waste is obtained. After verification， the overall recognition accuracy of construction waste can reach 94.88%. Compared with the traditional manual identification methods such as aerial photo monitoring and field investigation， the method studied in this paper has higher efficiency and recognition accuracy， which can provide a technical basis for real-time monitoring and accurate management of construction waste in the whole process. |
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| Keywords: | High-resolution remote sensing image Construction waste Transfer learning Automatic recognition Inception-V3 |
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