摘要
针对传统影像质量检查工作中积云提取存在人工作业量大、操作烦琐等问题,本文通过引入迁移学习机制,将已有数据集训练过程中得到的神经网络参数迁移到解译模型构建中,提出了一种适用于积云的自动提取方法。本文以湖南省不动产统一登记基础数据为实验对象进行了实验,结果表明,本文方法的浓积云提取总体精度可以达到90%以上,淡积云提取的总体精度可以达到87.3%,表明本文研究可用于高分影像积云自动提取。
In this paper,a cumulus automatic extraction method based on transfer learning is proposed to solve the problem of large amount of manual operations and cumbersome operation in cumulus extraction in traditional image quality inspection,which neural network parameters obtained during the training of existing data sets are migrated to the interpretation model construction. Based on the unified data of real estate registration in Hunan Province,the results show that the overall accuracy of thick and thick cumulonimbus extraction can reach more than 90%,and the overall accuracy of thin cloud extraction can reach 87.3%. This paper can be widely used in the automatic extraction of cumulus in high resolution images.
作者
楚彬
郝建明
华亮春
靳文凭
李政
CHU Bin;HAO Jianming;HUA Liangchun;JIN Wenping;LI Zheng(Hunan Institute of Surveying and Mapping Technology,Changsha 410007,China;Chinese Academy of Surveying and Mapping,Hunan Branch,Changsha 410007,China;Faculty of Geosciences and Environmental Engineering,Southwest Jiaotong University,Chengdu 611756,China)
出处
《测绘与空间地理信息》
2020年第3期93-96,共4页
Geomatics & Spatial Information Technology
关键词
积云提取
迁移学习
高分影像
面向对象分割
cumulus extract
transfer learning
high resolution images
object-oriented segmentation
