期刊论文

The classification by medium resolution remote sensing images based on deep learning algorithm of GoogleNet model

中文

测绘通报

0494-0911

2019

2019

6

29-33,40

10.13474/j.cnki.11-2246.2019.0179

中国科学院A类战略性先导科技专项(XDA20010302) 华中师范大学研究生教育创新资助项目(2018CXZZ001).

本校为第一机构

提出了一种基于深度学习技术的遥感分类方法,它能有效解决中分辨率影像在分类过程中出现的像元混分问题。研究选用2016年5月12日武汉市Landsat 7 ETM+遥感影像,基于GoogleNet模型中的Inceplion V3网络结构,借助迁移学习方法,构建出遥感分类模型,实现了对武汉市主城区4类典型地物(不透水层、植被、水体和其他用地)的自动分类提取,并将分类结果与传统最大似然分类(ML)结果进行了对比分析。研究表明:基于深度学习方法的遥感影像总体分类精度高达88.33%,Kappa系数为0.834 2,明显优于传统ML方法总体分类精度83%和Kappa系数0.755 0,而且有效抑制了地物在分类过程中出现的像元混分现象。

We proposed a remote sensing classification method based on deep learning technology,which can effectively solve the problem of pixel mixing in the medium resolution images classification.The research selected the Landsat 7 ETM+ remote sensing image of Wuhan City on May 12,2016.Based on the Inception V3 network structure in the GoogleNet model,the remote sensing image classification model was constructed by means of migration learning method,and four typical features of the main urban area of Wuhan were realized.Automatic classification of permeable layers,vegetation,water bodies and other lan...