利用Deeplab v3提取高分辨率遥感影像道路

针对传统道路提取方法存在的道路边缘粗糙、抗干扰性弱、提取精度低等问题,提出了一种基于编码解码器的空洞卷积模型(Deeplab v3)的道路提取方法。首先,对原始高分辨率遥感影像进行标注;其次,利用标注数据集对Deeplab v3模型进行训练、测试;最后,得到高分辨率遥感影像道路提取结果。分析结果可知,该模型能够较好地提取高分辨率遥感影像中的道路边缘特征,相比其他道路提取方法具有更高的提取精度和更加完整的道路信息,正确率可达到93%以上。

Road Extraction of High Resolution Remote Sensing Imagery Based on Deeplab v3

A new road extraction method based on the Deeplab v3 model is proposed to solve the problems of traditional road extraction methods such as rough road edge,weak anti-interference and low extraction accuracy existing.A three-step procedure is developed in this study for extracting roads based on high-resolution remote sensing image.Firstly,label the high-resolution remote sensing image.Secondly,the Deeplab v3 model is trained and tested by using the label data set.Finally,get the road extraction results of the high-resolution remote sensing image.The results indicate that the Deeplab v3 model can excellently extract the road edge features combined with the high-resolution remote sensing image.Compared with other road extraction methods,this proposed method displays more complete extracted road information and higher extraction accuracy,which has the accuracy over 93%.