Abstract
This work proposes MeCSAFNet, a multi-branch encoder-decoder architecture for land cover segmentation in multispectral imagery. The model separately processes visible and non-visible channels through dual ConvNeXt encoders, followed by individual decoders that reconstruct spatial information. A dedicated fusion decoder integrates intermediate features at multiple scales, combining fine spatial cues with high-level spectral representations. The feature fusion is further enhanced with CBAM attention, and the ASAU activation function contributes to stable and efficient optimization. The model is designed to process different spectral configurations, including a 4-channel (4c) input combining RGB and NIR bands, as well as a 6-channel (6c) input incorporating NDVI and NDWI indices. Experiments on the Five-Billion-Pixels (FBP) and Potsdam datasets demonstrate significant performance gains. On FBP, MeCSAFNet-base (6c) surpasses U-Net (4c) by +19.21%, U-Net (6c) by +14.72%, SegFormer (4c) by +19.62%, and SegFormer (6c) by +14.74% in mIoU. On Potsdam, MeCSAFNet-large (4c) improves over DeepLabV3+ (4c) by +6.48%, DeepLabV3+ (6c) by +5.85%, SegFormer (4c) by +9.11%, and SegFormer (6c) by +4.80% in mIoU. The model also achieves consistent gains over several recent state-of-the-art approaches. Moreover, compact variants of MeCSAFNet deliver notable performance with lower training time and reduced inference cost, supporting their deployment in resource-constrained environments.
Abstract (translated)
这项工作提出了MeCSAFNet，这是一种多分支编码器-解码器架构，用于处理多光谱图像中的土地覆盖分类。该模型通过双ConvNeXt编码器分别处理可见和不可见通道，并随后通过各自的解码器重建空间信息。一个专门的融合解码器在多个尺度上整合中间特征，将精细的空间线索与高级别的光谱表示结合在一起。特征融合进一步通过CBAM注意力机制增强，而ASAU激活函数则有助于稳定且高效的优化过程。 该模型被设计成可以处理不同的光谱配置，包括由RGB和NIR波段组合而成的4通道（4c）输入，以及包含NDVI和NDWI指数的6通道（6c）输入。在五千亿像素（FBP）和波茨坦数据集上的实验显示了显著的性能提升。在FBP上，MeCSAFNet-base (6c)相较于U-Net (4c)提高了+19.21%，相较于U-Net (6c)提高了+14.72%，相较于SegFormer (4c)提升了+19.62%，相较于SegFormer (6c)增加了+14.74%的mIoU。在波茨坦数据集上，MeCSAFNet-large (4c)相比于DeepLabV3+ (4c)提高了+6.48%，相比于DeepLabV3+ (6c)提高了+5.85%，相较于SegFormer (4c)提升了+9.11%，相较于SegFormer (6c)增加了+4.80%的mIoU。该模型还在几种最近的状态-of-the-art方法中取得了持续性的提升。 此外，MeCSAFNet的小型变体在训练时间和推理成本较低的情况下实现了显著的性能表现，支持它们在资源受限环境中部署。
URL
https://arxiv.org/abs/2602.10137
