A lightweight remote sensing image semantic segmentation method based on CBAM enhancement

doi:10.13474/j.cnki.11-2246.2025.1007

Abstract

Abstract: This study addresses the challenges in high-resolution remote sensing image semantic segmentation, such as large variations in object scales, blurred boundaries, and spectral similarity.A lightweight segmentation model is proposed, which integrates multi-scale features and dual attention mechanisms.The model is based on SegNeXt, incorporating a convolutional block attention module (CBAM)into its multi-scale convolutional attention network to refine feature representations through channel and spatial dual attention mechanisms.During the decoding stage, the Hamburger structure is used to integrate mid-to-high-level semantic information.Experiments on the GF-2 remote sensing image dataset show that the model achieves noticeable improvements over the original SegNeXt across various metrics, with particularly superior performance in handling fuzzy boundaries and linear feature categories.The results demonstrate that this method achieves a balance between accuracy and efficiency while maintaining a lightweight design, offering a feasible solution for real-time semantic interpretation of remote sensing images in resource-constrained environments.

Key words: remote sensing image, semantic segmentation, boundary enhancement, lightweight network, deep learning

CLC Number:

P237

ZHAO Xiaozu, GOU Changlong, YANG Yang. A lightweight remote sensing image semantic segmentation method based on CBAM enhancement[J]. Bulletin of Surveying and Mapping, 2025, 0(10): 36-42.

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