School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, China
Email: Lily_zhang588@outlook.com (L.Z.)
Manuscript received July 11, 2024; revised August 1, 2024; accepted August 14, 2024; published October 15, 2024
Abstract—Deep learning is a deep field of neural networks, and its application in remote sensing image classification and recognition processing has attracted attention and discussion from all walks of life. This paper first briefly introduces the traditional remote sensing image processing methods and the limitations of these algorithms and emphasizes the limitations of these techniques. Then, the research status of target recognition and change detection in remote sensing images based on deep learning is discussed, and how to select and design appropriate deep learning models. And then, the datasets of two different provinces were selected for comparative experiments, and the implementation process of target recognition and change detection in remote sensing images was described in detail. Finally, based on the experimental results, the future trend of deep learning application in remote sensing identification and classification is prospected.
Keywords—remote sensing image processing, deep learning, target recognition, change detection, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN)
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Cite: Li Zhang, "Research on Remote Sensing Image Target Recognition and Image Change Detection Algorithm Based on Deep Learning," International Journal of Future Computer and Communication, vol. 13, no. 4, pp. 60-66, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
(CC BY 4.0)
