遥感影像智能解译: 从监督学习到自监督学习

Abstract

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。

Alternate 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.