Cloud detection in satellite images is a vital step for cloud/land recognition, cloud/snow discrimination, and cloud shadow removal. Accurate cloud detection plays an important role in land resource management, environmental pollution monitoring, and land target recognition. Deep learning (DL) algorithms have shown great progress in cloud detection. However, as the complexity of the DL-based model increases, cloud detection efficiency decreases. DL-based cloud detection models are unable to successfully balance the performance-efficiency tradeoff. In our study, a multi-dimensional and multi-grained dense cascade forest (MDForest) is proposed for multi-spectral cloud detection. MDForest is a deep forest structure that automatically extracts low-level and high-level features from satellite cloud images end-to-end; a multi-dimensional and multi-grained scanning mechanism is introduced to capture the spectral information of multi-spectral satellite images while enhancing the representation learning ability of cascade forest. The experimental results on the HJ-1A/1B dataset show that MDForest improves the performance of cloud detection and possesses a good inference efficiency compared with DL-based cloud detection methods, which makes the proposed MDForest satisfy the application where good performance and high efficiency are both required.

中文翻译：

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基于多维多粒度密集级联森林的多光谱云检测

卫星图像中的云检测是云/地识别、云/雪识别和云阴影去除的重要步骤。准确的云检测在土地资源管理、环境污染监测、土地目标识别等方面具有重要作用。深度学习 (DL) 算法在云检测方面取得了巨大进步。然而，随着基于深度学习的模型复杂度的增加，云检测效率降低。基于 DL 的云检测模型无法成功平衡性能-效率权衡。在我们的研究中，提出了用于多光谱云检测的多维多粒度密集级联森林（MDForest）。MDForest是一种深度森林结构，可以端到端地自动从卫星云图像中提取低层和高层特征；引入多维多粒度扫描机制，在增强级联森林表征学习能力的同时，捕捉多光谱卫星图像的光谱信息。在 HJ-1A/1B 数据集上的实验结果表明，与基于 DL 的云检测方法相比，MDForest 提高了云检测的性能并具有良好的推理效率，这使得所提出的 MDForest 满足性能好、效率高的应用。两者都需要。