Abstract
Cloud occlusion severely degrades the semantic integrity of optical remote sensing imagery. While incorporating Synthetic Aperture Radar (SAR) provides complementary observations, achieving efficient global modeling and reliable cross-modal fusion under cloud interference remains challenging. Existing methods rely on dense global attention to capture long-range dependencies, yet such aggregation indiscriminately propagates cloud-induced noise. Improving robustness typically entails enlarging model capacity, which further increases computational overhead. Given the large-scale and high-resolution nature of remote sensing applications, such computational demands hinder practical deployment, leading to an efficiency-reliability trade-off. To address this dilemma, we propose EDC, an efficiency-oriented and discrepancy-conditioned optical-SAR semantic segmentation framework. A tri-stream encoder with Carrier Tokens enables compact global context modeling with reduced complexity. To prevent noise contamination, we introduce a Discrepancy-Conditioned Hybrid Fusion (DCHF) mechanism that selectively suppresses unreliable regions during global aggregation. In addition, an auxiliary cloud removal branch with teacher-guided distillation enhances semantic consistency under occlusion. Extensive experiments demonstrate that EDC achieves superior accuracy and efficiency, improving mIoU by 0.56\% and 0.88\% on M3M-CR and WHU-OPT-SAR, respectively, while reducing the number of parameters by 46.7\% and accelerating inference by 1.98$\times$. Our implementation is available at this https URL.
Abstract (translated)
云层遮挡会严重破坏光学遥感图像的语义完整性。尽管引入合成孔径雷达（SAR）可提供互补观测，但在云干扰下实现高效的全局建模与可靠的跨模态融合仍具挑战性。现有方法依赖密集的全局注意力来捕获长程依赖，但这种聚合会无差别地传播云层引起的噪声。提升鲁棒性通常需要扩大模型容量，这进一步增加了计算开销。鉴于遥感应用的大规模与高分辨率特性，此类计算需求阻碍了实际部署，导致效率与可靠性之间的权衡。为应对这一困境，我们提出EDC——一个面向效率与差异条件的光学-SAR语义分割框架。采用带载体令牌的三流编码器，以更低复杂度实现紧凑的全局上下文建模。为防止噪声污染，我们引入差异条件混合融合（DCHF）机制，在全局聚合过程中选择性地抑制不可靠区域。此外，配备教师引导蒸馏的辅助去云分支增强了遮挡下的语义一致性。大量实验表明，EDC实现了更优的准确性与效率，在M3M-CR和WHU-OPT-SAR数据集上分别将mIoU提升0.56%和0.88%，同时减少46.7%的参数数量并将推理速度提升1.98倍。我们的实现代码已公开于此https URL。
URL
https://arxiv.org/abs/2603.20811
