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Remote sensing image (RSI) scene classification is a hot topic in the field of remote sensing and has garnered a lot of attention. The key issue in image classification is effectively understanding semantic content. Convolutional neural networks (CNNs) are generally recognized to significantly improve classification performance due to their powerful feature extraction capabilities. However, the overall structure of the model is complicated and has a large number of parameters, making it difficult to extract more efficient features. To address these problems, in this paper, we propose a lightweight skip-connected expansion Inception network called SEINet. To capture characteristics at a more granular level, we create a new lightweight backbone network with fewer parameters based on the existing network architecture. Additionally, the paper introduces a skip-connected expansion Inception (SEI) module for extracting context-dependent relationships. The ablation experiments verify the effectiveness of our proposed module. Experiment findings on two public datasets demonstrate that our method has advantages in classification accuracy and execution efficiency over state-of-the-art (SOTA) methods.

A lightweight skip-connected expansion inception network for remote sensing scene classification