Multi-dimensional spatial pruning for remote sensing image scene classification

In recent years, remote sensing image classification tasks have garnered widespread attention and have been extensively studied by researchers. Most current studies focus on improving classification accuracy, leading to overly large and complex networks with high computational costs that are challenging to deploy for real-time remote sensing tasks. To address this issue, neural network pruning has emerged as an effective solution. However, existing pruning methods typically prune along a single dimension, and as the pruning ratio increases, important weights in that dimension often suffer from over-pruning, resulting in significant accuracy loss. This paper proposes a novel pruning method for remote sensing scene classification—Multidimensional Space Pruning (MSP). MSP performs stereoscopic pruning of filters along both channel and depth dimensions, simultaneously removing redundant information across two different dimensions. This prevents excessive pruning of important weights in a single dimension, thereby significantly reducing model complexity while maintaining accuracy. As a novel pruning method, MSP achieves remarkable results. At a pruning ratio of 0.4, MSP-pruned VGG-16 and ResNet-34 models on the NWPU-RESISC45 dataset show accuracy drops of only 1.05 % and 0.71 %, respectively, while achieving compression ratios of 92.52 % and 93.19 %. Similarly, on the AID dataset, the accuracy drops are merely 0.26 % and 0.54 %, with compression ratios reaching 96.23 % and 88.56 %, respectively. Experimental results on two public remote sensing image datasets demonstrate that compared to existing methods, MSP achieves higher compression ratios while maintaining model accuracy, showcasing superior model compression performance.