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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，样本库包含30 292张建筑垃圾和110 110张典型地物在内的共计140 402张样本。之后基于国际先进的深度学习环境Tensorflow，利用迁移学习在模型的最后一层重新输入了建筑垃圾等6类训练数据集，对Inception-V3模型进行了再训练，在较短时间内得到了建筑垃圾识别模型。随机抽取6 016张样本构成验证集逐个输入建筑垃圾识别模型，统计验证样本的模型识别结果构成混淆矩阵，得出该模型对所有地物的整体识别率K为97.43%， Kappa系数Ka为0.96，模型识别建筑垃圾的识别精确度Pv为99.10%，识别灵敏度为94.88%。与传统的航片监测、实地考察等纯人工识别方法相比，该方法所需时间较短且识别精度较高，有利于实现建筑垃圾的全过程实时监控和精准管理。 关键词： 高分遥感影像;  建筑垃圾;  迁移学习;  自动识别;  Inception?V3     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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