ArticleFrontiers in plant science2026
Hierarchical vision mamba U-net for farmland semantic segmentation from remote sensing imagery.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Farmland semantic segmentation (FSS) from remote sensing imagery (RSIs) is a critical yet challenging task in precision agriculture. CNN-based methods suffer from limited receptive fields and poor long-range dependency modeling, while Transformers are limited by quadratic computational complexity. To address these issues, this paper proposes a hierarchical Vision Mamba U-Net (HVM-UNet) integrating three key components: a cross-scanning Visual State Space (CSVSS) block to improve scanning performance, a lightweight global feature fusion (GFF) module to replace traditional skip connections for enhanced detail preservation, and a multiscale spatial attention module (MSSA) for refined feature aggregation. Extensive experiments on benchmark datasets demonstrate that HVM-UNet achieves superior segmentation accuracy with linear complexity, outperforming both CNN-based and Transformer-based approaches and offering a robust solution for precision agriculture.
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