Level merging attention based on dense network for remote sensing image scene classification

Zhi Li; Ke Zheng; Li Ni; Lianru Gao

doi:10.1117/12.3010658

15 November 2023 Level merging attention based on dense network for remote sensing image scene classification

Zhi Li, Ke Zheng, Li Ni, Lianru Gao

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128150D (2023) https://doi.org/10.1117/12.3010658
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

In the intelligent extraction of high-resolution remote sensing images, the scene classification of remote sensing images has great application prospects in many application fields. In past research, convolutional neural networks have shown great potential, and the introduction of the attention mechanism can further improve the feature representation ability in CNNs-based models. This letter proposes a new network architecture for remote sensing image scene classification. By introducing two attention mechanisms with different structures at different operation levels, the attention mechanism is merged at the level, and the performance of the basic network structure is effectively improved by using the new network architecture constructed. The overall accuracy show that the two attention structures can enhance the feature extraction effect overall and the details effectively. Experimental results on two public datasets, UC Merced and NWPU-RESISC45, show that the proposed model outperforms the current state-of-the-art methods in classification accuracy.

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

Citation Download Citation

Zhi Li, Ke Zheng, Li Ni, and Lianru Gao "Level merging attention based on dense network for remote sensing image scene classification", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128150D (15 November 2023); https://doi.org/10.1117/12.3010658

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KEYWORDS

Feature extraction

Remote sensing

Scene classification

Education and training

Convolution

Convolutional neural networks

Image processing

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