A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

被引：9
作者：
Bai, Chenshuai [1 ]
Bai, Xiaofeng [1 ]
Wu, Kaijun [1 ]
机构：
[1] Lanzhou Jiaotong Univ, Elect & Informat Engn, Lanzhou 730070, Peoples R China
来源：
ELECTRONICS | 2023年 / 12卷 / 24期
关键词：
deep learning; optical remote sensing image; object detection; comparative analysis of performance; TARGET DETECTION; VEHICLE DETECTION; CLASSIFICATION; SAR;
D O I：
10.3390/electronics12244902
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Target detection in optical remote sensing images using deep-learning technologies has a wide range of applications in urban building detection, road extraction, crop monitoring, and forest fire monitoring, which provides strong support for environmental monitoring, urban planning, and agricultural management. This paper reviews the research progress of the YOLO series, SSD series, candidate region series, and Transformer algorithm. It summarizes the object detection algorithms based on standard improvement methods such as supervision, attention mechanism, and multi-scale. The performance of different algorithms is also compared and analyzed with the common remote sensing image data sets. Finally, future research challenges, improvement directions, and issues of concern are prospected, which provides valuable ideas for subsequent related research.
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收藏
页数：23
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