Local Similarity-Driven Refinement for Model-Agnostic Ground-Based Cloud Detection

Cloud cover estimation is of crucial significance in meteorological observations and short-term/long-term weather forecasting, as it directly affects the accuracy of radiation balance assessment, precipitation prediction, and climate change modeling. Ground-based automated cloud quantification observation instruments enable continuous, high-resolution cloud monitoring with spatial-temporal continuity that satellite remote sensing cannot fully achieve, highlighting the immense value of ground-based cloud image processing for practical meteorological applications. However, existing cloud detection methods predominantly rely on supervised training with ground truth masks, which overlook the rich contextual information and inherent regularization constraints embedded in original cloud images. This oversight frequently results in mismatched cloud boundaries, inadequate model interpretability, and poor adaptability to complex cloud morphologies—particularly for thin clouds and cirrus clouds characterized by weak grayscale contrast, sparse texture, and irregular shapes. Consequently, these limitations lead to suboptimal detection performance, including under-segmentation or over-segmentation, and further induce inaccuracies in quantitative cloud cover estimation.

To address the aforementioned issues and achieve accurate cloud cover detection results, this study proposes a model-agnostic refinement method designed to optimize the coarse detection masks generated by any pre-trained cloud detection model. The framework is jointly optimized by three loss functions: a local similarity descriptor, total variation (TV) regularization, and a traditional detection loss (e.g., cross-entropy). Specifically, the local similarity descriptor is defined as the difference between two terms: the average grayscale difference of each pixel and cloud region and background pixels within a local window. This descriptor effectively enhances the discriminability between cloud and non-cloud regions at the local level. The total variation regularization term is introduced to maintain the smoothness of the detection boundary and suppress spurious noise. The cross-entropy loss ensures the overall consistency between the refined result and the ground truth.

Minimizing the combined loss function drives the coarse detection result to evolve adaptively along the actual cloud boundary, thereby achieving more precise alignment with the true cloud contours. Notably, the proposed framework elevates the detection of thin clouds and cirrus clouds, effectively mitigating missed detection areas in these tenuous cloud structures. Furthermore, the integrated loss function enhances model interpretability: the local similarity descriptor explicitly quantifies the differences within local window, and minimizing this term inherently refines the detection by strengthening the distinction between cloud and background regions. Ultimately, the refined detection results substantially improve the accuracy of cloud cover estimation, laying a solid foundation for reliable meteorological observations and weather forecasting applications.