论文标题
在遥感图像中进行变更检测的深度学习：全面审查和荟萃分析
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis
论文作者
论文摘要
在过去几年中，深度学习（DL）算法被视为一种遥感图像分析的选择方法。由于其有效的应用，还引入了深度学习以进行自动变更检测并取得了巨大的成功。本研究试图对该子领域的最新进展进行全面的审查和荟萃分析。具体而言，我们首先介绍了深度学习方法的基本原理，这些方法是用于变更检测的。其次，我们介绍了用于检查变化检测DL研究状态的荟萃分析的细节。然后，我们通过对现有方法的一般概述进行一般概述，专注于基于学习的更改检测方法。具体而言，这些基于深度学习的方法分为三组。完全有监督的基于学习的方法，完全无监督的基于学习的方法和基于转移学习的技术。由于这些调查，确定了有希望的新方向以进行未来的研究。这项研究将以几种方式为我们的理解以了解变更检测，并为进一步的研究提供基础。
Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research.
