Efficient remote sensing image classification using the novel STConvNeXt convolutional network

作者：Bo Liu, Chenmei Zhan, Cheng Guo, Xiaobo Liu, Shufen Ruan · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-92629-x · 被引用次数：21 · 研究领域：Remote-Sensing Image Classification、Image Retrieval and Classification Techniques、Advanced Image and Video Retrieval Techniques

Remote sensing images present formidable classification challenges due to their complex spatial organization, high inter-class similarity, and significant intra-class variability. To address the balance between computational efficiency and feature extraction capability in existing methods, this paper innovatively proposes a lightweight convolutional network, STConvNeXt. In its architectural design, the model incorporates a split-based mobile convolution module with a hierarchical tree structure. It employs parameterized depthwise separable convolutions to reduce computational complexity and constructs a multi-level feature tree to facilitate cross-scale feature fusion. For feature enhancement, a fast pyramid pooling module replaces the traditional spatial pyramid structure, effectively reducing the number of parameters while preserving large-scale contextual awareness. In terms of training strategy, a dynamic threshold loss function is introduced, utilizing a learnable inter-class margin to improve the model's ability to distinguish difficult-to-classify samples. Systematic experiments on the UCMerced, AID, and NWPU-RESISC45 benchmark datasets validate the effectiveness of the proposed approach: compared with the ConvNeXt baseline, STConvNeXt reduces both parameter count (by 56.49%) and FLOPs (by 49.89%), while improving classification accuracy by 1.2-2.7%. Furthermore, compared with the current state-of-the-art remote sensing scene classification models, our method still exh...