遥感影像智能解译:从监督学习到自监督学习

doi:10.11947/j.AGCS.2021.20210089

测绘学报 ›› 2021, Vol. 50 ›› Issue (8): 1122-1134.doi: 10.11947/j.AGCS.2021.20210089

遥感影像智能解译:从监督学习到自监督学习

陶超^1,2, 阴紫薇^1,2, 朱庆³, 李海峰^1,2

1. 中南大学地球科学与信息物理学院, 湖南长沙 410083;
2. 中南大学有色金属成矿预测与地质环境监测教育部重点实验室, 湖南长沙 410083;
3. 西南交通大学地球科学与环境工程学院, 四川成都 611756

收稿日期:2021-02-20 修回日期:2021-07-25 发布日期:2021-08-24
通讯作者: 李海峰 E-mail:lihaifeng@csu.edu.cn
作者简介:陶超(1985-),男,教授,博士生导师,研究方向为遥感影像智能解译和机器学习。
基金资助:
国家重点研发计划（2018YFB0504500）；国家自然科学基金（41771458；41871364）；湖湘青年英才计划（2018RS3012）；湖南省教育厅创新平台开放基金（18K005）；湖南省研究生科研创新项目（CX20200325）；中南大学中央高校基本科研业务费专项资金（2020zzts671）

Remote sensing image intelligent interpretation: from supervised learning to self-supervised learning

TAO Chao^1,2, YIN Ziwei^1,2, ZHU Qing³, LI Haifeng^1,2

1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China;
3. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

Received:2021-02-20 Revised:2021-07-25 Published:2021-08-24
Supported by:
The National Key Research and Development Program (No. 2018YFB0504500);The National Natural Science Foundation of China (Nos. 41771458;41871364);The Young Elite Scientists Sponsorship Program by Hunan Province of China (No. 2018RS3012);Hunan Science and Technology Department Innovation Platform Open Fund Project (No. 18K005);The Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20200325);The Fundamental Research Funds for the Central Universities of Central South University (No. 2020zzts671)

摘要/Abstract

摘要： 遥感影像精准解译是遥感应用落地的核心和关键技术。近年来，以深度学习为代表的监督学习方法凭借其强大的特征学习能力，在遥感影像智能解译领域较传统方法取得了突破性进展。这一方法的成功严重依赖于大规模、高质量的标注数据，而遥感影像解译对象独特的时空异质性特点使得构建一个完备的人工标注数据库成本极高，这一矛盾严重制约了以监督学习为基础的遥感影像解译方法在大区域、复杂场景下的应用。如何破解遥感影像精准解译“最后一千米”已成为业界亟待解决的问题。针对该问题，本文系统地总结和评述了监督学习方法在遥感影像智能解译领域的主要研究进展，并分析其存在的不足和背后原因。在此基础上，重点介绍了自监督学习作为一种新兴的机器学习范式在遥感影像智能解译中的应用潜力和主要研究问题，阐明了遥感影像解译思路从监督学习转化到自监督学习的意义和价值，以期为数据源极大丰富条件下开展遥感影像智能解译研究提供新的视角。

关键词: 遥感影像智能解译, 监督学习, 深度学习, 自监督学习

Abstract: Accurate interpretation of remote sensing image (RSI) plays a vital role in the implementation of remote sensing applications. In recent years, deep supervised learning has achieved great success in the field of RSI interpretation by its soaring performance on representation learning. However, this method heavily relies on large-scale and high-quality labeled data, while building a big remote sensing data set is extremely expensive because of the unique spatial-temporal heterogeneity of remote sensing data. This contradiction seriously restricts the performance of deep supervised learning in large areas and complicated remote sensing scenes. How to solve the last mile problem in the field of RSI accurate interpretation becomes urgent. This paper first systematically reviews the main research progress of supervised learning methods in the field of RSI interpretation, and then analyzes its limitations. Afterward, we introduce the concept of self-supervised learning and detail how it works for unsupervised feature learning. Finally, we briefly discuss open problems and future directions of self-supervised learning if it is applied in the field of RSI interpretation, with the aim of providing a new perspective for RSI interpretation with the adoption of huge unlabeled data.

Key words: remote sensing image intelligent interpretation, supervised learning, deep learning, self-supervised learning

中图分类号:

P231

陶超, 阴紫薇, 朱庆, 李海峰. 遥感影像智能解译:从监督学习到自监督学习[J]. 测绘学报, 2021, 50(8): 1122-1134.

TAO Chao, YIN Ziwei, ZHU Qing, LI Haifeng. Remote sensing image intelligent interpretation: from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1122-1134.

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遥感影像智能解译:从监督学习到自监督学习

Remote sensing image intelligent interpretation: from supervised learning to self-supervised learning

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