## 1 Introduction
CAMs provide a way to understand which features the model is focusing on when making predictions, making the models more transparent and trustworthy for researchers and practitioners in the field of cloud detection. CAMs generate coarse heatmaps by aggregating high-level convolutional features through global average pooling (GAP), highlighting areas that contribute most to the classification output [20]. Global average pooling (GAP) is a pooling operation that computes the spatial average of each feature map, reducing each channel to a single scalar value and eliminating the need for fully connected layers in classification tasks. Nevertheless, standard CAM implementations generate low-resolution activation maps (typically 14 \(\times\) 14 pixels) that fail to precisely localize cloud boundaries while often misclassifying bright surfaces as clouds due to gradient saturation effects [21]. While gradient-weighted CAM (Grad CAM and Grad-CAM++) [22; 23] improves upon traditional CAMs by using higher-order gradients, its computational overhead increases training time by 40% [24]. The subsequent HiResCAM [23] paradigm eliminated global pooling dependencies to preserve high-resolution details, proving particularly effective for ground-based cloud segmentation tasks, albeit with heightened sensitivity to shallow-layer gradient noise [25]. Concurrently, FullGrad [20] adopted a comprehensive gradient aggregation strategy across all network layers, enhancing attribution faithfulness but requiring significant memory overhead due to full-network backpropagation [26]. Most recently, LayerCAM [27] emerged as a multi-scale solution, dynamically fusing hierarchical features to balance semantic precision and spatial detail, though necessitating careful layer contribution tuning to avoid artifact propagation [28]. Score-CAM [29] is a visualization technique for interpreting deep-learning model decisions that generates heat maps of regions critical to model predictions by introducing score-weighted activation maps prior to the output of a specific class. The core idea of this approach is to generate more accurate class activation maps (CAMs) by utilizing the activation maps from the forward propagation process and weighting them by the scores of the target classes. The main advantage of Score-CAM over the traditional Grad-CAM method is that it does not rely on gradient information for computation but generates heat maps by weighting the feature maps during the forward propagation process, which makes it able to provide more accurate localization information when dealing with images with rich details [23]. In addition, Score-CAM is not affected by the gradient saturation problem, which typically causes gradient-based methods to produce a weaker response in some important regions of the image, thus improving its ability to capture image details. For example, several studies have shown that Score-CAM provides clearer and more accurate feature localization than Grad-CAM when dealing with challenging cloud segmentation scenarios such as thin cirrus clouds over bright backgrounds or heterogeneous cloud formations, which is particularly important for precise cloud coverage estimation in meteorological applications [20]. These XAI methods have significantly improved the interpretability of deep-learning models in cloud segmentation, allowing researchers to better understand the decision-making process of the models and increasing trust in their predictions. However, existing CAM implementations still have limitations in terms of multi-scale feature integration and pixel-level segmentation tasks. Moreover, the existing CAM implementations have three key limitations: (1) they only consider pre-fully connected layer features, (2) they lack multi-scale feature integration, and (3) they only focus on classification targets and perform poorly in pixel-level segmentation tasks, particularly in ground-based cloud image analysis (as shown in Figure 1).