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

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...

Automatic Recognition Method of Construction Waste based on Transfer Learning and Retraining Model and High-score Remote Sensing Data

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.