融合卷积与Mamba，提升遥感图像云检测精度
CD-Mamba: Cloud detection with long-range spatial dependency modeling
- 结合卷积捕捉局部纹理与Mamba建模长程依赖
- 在多个数据集上优于现有方法，显著提升检测准确率
- 适合需要高精度云检测的遥感图像分析任务
遥感图像常被云层遮挡，严重影响数据完整性和可靠性。有效的云检测需同时处理短程空间冗余和云块间的长程大气相似性。卷积神经网络擅长捕捉局部空间依赖，而Mamba在建模长程依赖方面表现优异。为此，我们提出CD-Mamba，一种将卷积与Mamba状态空间建模融合的统一云检测网络。该设计能全面捕获像素级纹理细节与长期块级依赖关系，同时处理像素级交互与大范围块级依赖，提升多尺度下的检测精度。大量实验验证了其有效性，并证明其性能优于现有方法。
原文摘要 · Abstract (English)
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.
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