Dynamic pruning with maximum entropy reinforcement learning for geological environment remote sensing interpretation
Author Identifier (ORCID)
Jianxin Li: https://orcid.org/0000-0002-9059-330X
Abstract
The geological environment remote sensing interpretation task imposes higher demands on deep learning models' computational efficiency and deployment performance. As an efficient lightweight strategy, channel pruning has been widely applied in computer vision. However, when applied to geological remote sensing tasks, existing pruning methods struggle to balance interpretation accuracy and computational cost effectively, mainly due to the complexity of geological environment data and the constraints of resource-limited deployment. Moreover, although reinforcement learning-based pruning strategies have been proposed to overcome these limitations, they often suffer from limited exploration capability and unstable training performance. To address these issues, we propose a dynamic pruning method (SACRL) incorporating the maximum entropy mechanism, which enhances the explorability and stability of the pruning strategy by introducing an entropy regularity term in the reinforcement learning objective function. Experiments demonstrate that, compared to conventional pruning methods such as AMC and Taylor, SACRL demonstrates better performance in terms of overall classification accuracy (OA), mean Intersection over Union (mIoU), and mean Average Precision (mAP). Furthermore, deploying the pruned model on resourcelimited devices, such as the Jetson AGX Orin, further validates its adaptability in real-world applications and highlights its potential in remote sensing interpretation of geological environments.
Document Type
Conference Proceeding
Date of Publication
1-1-2025
Publication Title
2025 IEEE International Conference on High Performance Computing and Communications (HPCC)
Publisher
IEEE
School
School of Business and Law
Funders
National Natural Science Foundation of China (42311530065, U21A2013) / China University of Geosciences (2023080)
Copyright
subscription content
First Page
528
Last Page
535
Comments
Jing, H., Du, H., Huang, X., Wang, Y., Jin, M., Li, J., Chen, Y., & Li, J. (2025). Dynamic pruning with maximum entropy reinforcement learning for geological environment remote sensing interpretation. In 2025 IEEE International Conference on High Performance Computing and Communications (HPCC) (pp. 528–535). IEEE. https://doi.org/10.1109/HPCC67675.2025.00085