Bibliometric and visualized analysis of deep learning in remote sensing. Issue 15 (18th August 2022)

• Record Type: Journal Article
• Authors: Bai, Yang; Sun, Xiyan; Ji, Yuanfa; Huang, Jianhua; Fu, Wentao; Shi, Huien
• Is Part Of: International journal of remote sensing. Volume 43:Issue 15/16(2022)
• Page Start: 5534
• Page End: 5571
• Publication Date: 2022-08-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1949069
• Languages: English
• ISSNs: 0143-1161

Abstract

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.