遥感影像智能解译样本库现状与研究

Status analysis and research of sample database for intelligent interpretation of remote sensing image

我国遥感对地观测等项目顺利实施,获取了大量时效性强、覆盖范围广、信息量丰富的遥感数据。但遥感影像智能化自动处理技术发展仍相对滞后,无法满足区域/全球大范围地物信息快速提取的需求。近年来,人们利用深度学习技术显著提高了影像特征提取成效,但由于所使用的深度学习样本数量和类型有限,对于多源遥感影像的自动解译能力仍然不足。本文面向大范围多源遥感影像地物信息智能解译需求,在分析现有样本集现状及问题的基础上,研究提出遥感影像智能解译样本库设计方案,并在此基础上设计了基于互联网的样本协同采集与共享服务框架。本文将为多源遥感影像样本库建设提供参考,为大范围遥感影像智能解译提供支持。

The rapid development of earth observation projects in China has obtained a large volume of multi-source(multi-type sensors,multi-temporal,multi-scale)remote sensing data.But the capability of intelligent remote sensing image processing lags behind data acquisition.In recent years,people have significantly improved the effectiveness of image feature extraction with deep learning networks.But limited number and variety of sample data is not enough for processing the multi-source remote sensing images.This paper analyzed existing sample datasets and proposed a method for constructing a sample database for intelligent remote sensing image interpretation,including the data model,classification system,data organization,as well as the Internet-based platform for collaborative sample collection and sharing.