Research Trend of the Remote Sensing Image Analysis Using Deep Learning

被引:5
作者
Kim, Hyungwoo [1 ]
Kim, Minho [2 ]
Lee, Yangwon [1 ]
机构
[1] Pukyong Natl Univ, Dept Spatial Informat Engn, Div Earth Environm Syst Sci, Busan, South Korea
[2] Sangmyung Univ, Coll Space Environm, Seoul, South Korea
关键词
Deep learning; Remote sensing; Image analysis; CNN;
D O I
10.7780/kjrs.2022.38.5.3.2
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.
引用
收藏
页码:819 / 834
页数:16
相关论文
共 56 条
[1]   An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images [J].
Abdollahi, Abolfazl ;
Pradhan, Biswajeet ;
Alamri, Abdullah M. .
GEOCARTO INTERNATIONAL, 2022, 37 (12) :3355-3370
[2]   Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management [J].
Abid, Sheikh Kamran ;
Sulaiman, Noralfishah ;
Chan, Shiau Wei ;
Nazir, Umber ;
Abid, Muhammad ;
Han, Heesup ;
Ariza-Montes, Antonio ;
Vega-Munoz, Alejandro .
SUSTAINABILITY, 2021, 13 (22)
[3]  
Alem Abebaw, 2020, 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), P903, DOI 10.1109/ICRITO48877.2020.9197824
[4]   Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review [J].
Nilani Algiriyage ;
Raj Prasanna ;
Kristin Stock ;
Emma E. H. Doyle ;
David Johnston .
SN Computer Science, 2022, 3 (1)
[5]   A Hybrid Deep ResNet and Inception Model for Hyperspectral Image Classification [J].
Alotaibi, Bandar ;
Alotaibi, Munif .
PFG-JOURNAL OF PHOTOGRAMMETRY REMOTE SENSING AND GEOINFORMATION SCIENCE, 2020, 88 (06) :463-476
[6]  
[Anonymous], 1982, Competition and Cooperation in Neural Nets, DOI DOI 10.1007/978-3-642-46466-9_18
[7]   Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami [J].
Bai, Yanbing ;
Mas, Erick ;
Koshimura, Shunichi .
REMOTE SENSING, 2018, 10 (10)
[8]  
Brand A., 2021, Int. Arch. Photogrammetry Remote Sens. Spatial Inf. Sci., V43, P47, DOI [10.5194/isprs-archives-XLIII-B3-2021-47-2021, DOI 10.5194/ISPRSARCHIVES-XLIII-B3-2021-47-2021]
[9]   Uncovering Ecological Patterns with Convolutional Neural Networks [J].
Brodrick, Philip G. ;
Davies, Andrew B. ;
Asner, Gregory P. .
TRENDS IN ECOLOGY & EVOLUTION, 2019, 34 (08) :734-745
[10]   Remote Sensing Image Change Detection With Transformers [J].
Chen, Hao ;
Qi, Zipeng ;
Shi, Zhenwei .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60