CD-Mamba：基于长距离空间依赖建模的云检测
CD-Mamba: Cloud detection with long-range spatial dependency modeling
AI总结：
针对遥感图像云检测需兼顾局部与长程空间依赖的问题，提出融合卷积与Mamba状态空间建模的混合云检测模型CD-Mamba，可同时捕捉像素级细节与长程斑块依赖，经实验验证性能优于现有方法。
摘要：
遥感图像常被云层遮挡，给数据完整性与可靠性带来重大挑战。有效的云检测需同时应对云斑块间的短程空间冗余与长程大气相似性。卷积神经网络擅长捕捉局部空间依赖，而Mamba在长程依赖建模方面能力突出。为充分利用局部空间关系与长程依赖，我们提出CD-Mamba——一种将卷积与Mamba的状态空间建模整合到统一云检测网络中的混合模型。CD-Mamba旨在全面捕捉像素级纹理细节与长程斑块级依赖以用于云检测，该设计使其能同时处理像素级交互与大范围斑块级依赖，提升不同空间尺度下的检测精度。大量实验验证了CD-Mamba的有效性，并证明其性能优于现有方法。
英文摘要：
Remote sensing images are frequently obscured by cloud cover, posing significant challenges to data integrity and reliability. Effective cloud detection requires addressing both short-range spatial redundancies and long-range atmospheric similarities among cloud patches. Convolutional neural networks are effective at capturing local spatial dependencies, while Mamba has strong capabilities in modeling long-range dependencies. To fully leverage both local spatial relations and long-range dependencies, we propose CD-Mamba, a hybrid model that integrates convolution and Mamba's state-space modeling into a unified cloud detection network. CD-Mamba is designed to comprehensively capture pixelwise textural details and long term patchwise dependencies for cloud detection. This design enables CD-Mamba to manage both pixel-wise interactions and extensive patch-wise dependencies simultaneously, improving detection accuracy across diverse spatial scales. Extensive experiments validate the effectiveness of CD-Mamba and demonstrate its superior performance over existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
