多光谱遥感图像分割新模型，融合可见与非可见波段信息，提升地表分类精度。
Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation
- 双分支编码器分别处理可见光与非可见光通道，提升多光谱特征提取能力
- 6通道输入在FBP数据集上比U-Net高14.72%的mIoU，性能显著超越现有方法
- 轻量级版本训练快、推理成本低，适合边缘部署
本文提出MeCSAFNet，一种用于多光谱影像地表覆盖分割的多分支编码器-解码器架构。模型通过双ConvNeXt编码器分别处理可见光与非可见光波段，各配独立解码器重建空间信息。专用融合解码器在多尺度整合中间特征，结合精细空间线索与高层光谱表示。特征融合引入CBAM注意力机制，ASAU激活函数保障优化稳定高效。模型支持4通道（RGB+NIR）和6通道（含NDVI、NDWI）输入配置。在Five-Billion-Pixels（FBP）和Potsdam数据集上的实验表明，性能显著提升：在FBP上，MeCSAFNet-base（6c）相比U-Net（4c）提升+19.21%，相比SegFormer（6c）提升+14.74%；在Potsdam上，MeCSAFNet-large（4c）相比DeepLabV3+（4c）提升+6.48%，相比SegFormer（6c）提升+4.80%。同时，紧凑型变体在保持高性能的同时降低训练时间与推理开销，适用于资源受限场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。