A remote sensing image classification method based on residual network and attention mechanism

Xinpeng Man; Yinglei Song

doi:10.1117/12.3010291

15 November 2023 A remote sensing image classification method based on residual network and attention mechanism

Xinpeng Man, Yinglei Song

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281502 (2023) https://doi.org/10.1117/12.3010291
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

In the process of remote sensing image classification, remote sensing image itself has the characteristics of complex spatial relationship and diverse information in the image. This paper puts forward a remote sensing image classification method based on the residual network ResNet50, adding channel attention and spatial attention mechanism, replace the full connection layer. The experimental results prove its feasibility, and when the remote sensing image data does not meet the independent and identical distribution, it can still get better classification effect when it is input into the improved ResNet50, so as to further process the remote sensing image.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinpeng Man and Yinglei Song "A remote sensing image classification method based on residual network and attention mechanism", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281502 (15 November 2023); https://doi.org/10.1117/12.3010291

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KEYWORDS

Remote sensing

Education and training

Image processing

Data modeling

Image classification

Image enhancement

Linear filtering

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