## 1 Introduction  
Cloud cover significantly impacts ground-based astronomical observations by attenuating incoming starlight, distorting photometric measurements, and reducing observational efficiency [1]. Studies demonstrate that even thin cirrus clouds (optical depth \(<\) 0.3) can introduce photometric errors exceeding 0.1 magnitudes, substantially affecting precision measurements [2]. The presence of clouds also alters sky brightness distribution, complicating background subtraction in wide-field surveys [3]. Traditional ground-based cloud detection methodologies face three fundamental challenges. Threshold-based approaches relying on RGB ratios or intensity thresholds [4] exhibit strong diurnal performance variation. Their accuracy drops from 85% at noon to below 60% during twilight due to rapidly changing illumination conditions [5]. Texture-analysis methods using wavelet transforms or LBPs [6] show limited generalization across cloud types. While achieving 92% accuracy for cumulus clouds, their performance degrades to 68-72% for stratus and cirrostratus formations [7]. This limitation stems from their inability to capture the diffuse boundaries characteristic of high-altitudeclouds [8]. Physical model-based techniques incorporating radiative transfer calculations [9] require precise knowledge of atmospheric parameters that are rarely available in operational scenarios [10]. Recent comparative studies [11] highlight that these conventional methods exhibit mean absolute errors of 18-25% in cloud fraction estimation under realistic observing conditions. Cloud thickness presents additional challenges for segmentation algorithms, as optically thin clouds (\(\tau<0.3\)) exhibit significantly different scattering properties compared to thick cumulus layers (\(\tau>10\)) [12]. Studies show that traditional methods underestimate thin cirrus coverage by 20-40% due to their semi-transparent nature [13] while frequently overestimating thick cloud boundaries by 5-15% from shadowing effects [14]. Recent advancements in deep learning have significantly enhanced the field of ground-based cloud image detection and segmentation. Deep-learning approaches, particularly those leveraging convolutional neural networks (CNNs) and fully convolutional networks (FCNs), have demonstrated remarkable efficacy in this domain. According to [PERSON] et al. [15], CNNs are capable of automatically extracting hierarchical features from images, which has led to substantial improvements in the accuracy of cloud detection and segmentation. Additionally, [PERSON] et al. [16] highlighted the effectiveness of FCNs in achieving pixel-level predictions, which is crucial for the precise delineation of cloud boundaries. These methods have also shown a strong ability to generalize across different imaging conditions, as noted by [PERSON] et al. [17], who emphasized the importance of skip connections in U-Net architectures for retaining critical spatial information. Furthermore, the integration of attention mechanisms into deep-learning models, as discussed by [PERSON] et al. [18], has further improved the performance of cloud segmentation by allowing the model to focus on the most relevant features. The development of more sophisticated architectures and the optimization of training strategies have collectively contributed to the robustness and reliability of deep-learning-based cloud image analysis. However, these deep-learning models often operate as \"black boxes,\" lacking interpretability in their decision-making processes. This lack of interpretability poses significant challenges for validating results in operational meteorology and climate science [19]. To address this limitation, class activation maps (CAMs) have been introduced to visualize the regions influencing model predictions, thereby enhancing the interpretability of deep-learning models in cloud segmentation tasks.