RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification
Guangwenjie Zou, Liang Yao, Fan Liu, Chuanyi Zhang, Xin Li, and Ning Chen, Shengxiang Xu, Jun Zhou

TL;DR
RemoteTrimmer introduces an adaptive structural pruning method tailored for remote sensing image classification, effectively reducing model complexity while maintaining high accuracy, and outperforming existing approaches.
Contribution
The paper presents a novel adaptive pruning strategy and a specialized loss function for fine-tuning, addressing the unique challenges of remote sensing image data.
Findings
Achieves minimal accuracy loss after pruning
Outperforms state-of-the-art methods on remote sensing datasets
Effective in maintaining model performance with reduced complexity
Abstract
Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing images, resulting in significant accuracy loss after pruning. To this end, we propose an effective structural pruning approach for remote sensing image classification. Specifically, a pruning strategy that amplifies the differences in channel importance of the model is introduced. Then an adaptive mining loss function is designed for the fine-tuning process of the pruned model. Finally, we conducted experiments on two remote sensing classification datasets. The experimental results demonstrate that our method achieves minimal accuracy loss after compressing remote sensing…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Metaheuristic Optimization Algorithms Research · Water Quality Monitoring Technologies
MethodsPruning
