ABSTRACT

Deep learning (DL) has been proven to be a powerful method in computer vision and is receiving increasing attention in remote sensing. It is important to analyse the research progress, hotspots, trends and methods in the field of deep learning in remote sensing. First, the main research countries (11), research institutions (20), researchers (20), and the most cited references (20) and hotspots (8) in this field were identified by analysing a total of 2,467 published papers with the bibliometric and visualized analysis (BVA) method. Then, based on the above analysis results, the research basis and the progress of hotspots in this field were summarized by reading a total of 181 relevant papers in detail with the traditional literature combing (TLC) method. The results indicate that deep learning is becoming an important tool for remote sensing and has been widely used in the vast majority of remote sensing tasks related to image processing. Among the following deep learning methods, the convolutional neural network (CNN) is undoubtedly the most widely used model.

摘要

深度学习 (DL) 已被证明是计算机视觉中的一种强大方法，并且在遥感中受到越来越多的关注。分析遥感深度学习领域的研究进展、热点、趋势和方法具有重要意义。首先，通过文献计量学对2467篇已发表论文进行分析，确定了该领域的主要研究国家（11个）、研究机构（20个）、研究人员（20个）、引用次数最多的参考文献（20个）和热点（8个）。和可视化分析（BVA）方法。然后，基于上述分析结果，采用传统文献梳理（TLC）方法，详细阅读共181篇相关论文，总结了该领域的研究基础和热点进展。结果表明，深度学习正在成为遥感的重要工具，并已广泛应用于绝大多数与图像处理相关的遥感任务。在以下深度学习方法中，卷积神经网络（CNN）无疑是应用最广泛的模型。