## 3 Methods
### Framework Architecture  
The architectural framework of our proposed model, highlighting its key innovative components, is illustrated in Figure 4. The schematic demonstrates the multi-scale input strategy implemented in the second and third encoding layers, where low-resolution input images are first normalized through \"Conv+BN+ReLU\" operations. The operation is a sequential combination of a convolutional layer, batch normalization, and ReLU activation function, which performs feature extraction while stabilizing training dynamics and introducing non-linearity. These normalized inputs are then channel-wise concatenated with downsampled features from preceding layers, forming a combined representation that undergoes \(1\times 1\) convolutional compression for dimensionality reduction. The diagram further delineates the integration of multi-scale image feature blocks, which process these fused features to enhance hierarchical representation. This design effectively captures both  
Figure 3: The structure of multi-scale CAM. local fine-grained details and broader contextual information, addressing the limitations in traditional single-scale feature extraction methods and cloud segmentation architectures. The schematic diagram illustrates three key architectural advancements. Firstly, the multi-scale feature integration module enhances feature diversity through an adaptive hierarchical fusion strategy. In the second and third encoding layers, input images at different resolutions (224 \(\times\) 224 and 112 \(\times\) 112 pixels) are processed in parallel streams. Each stream starts with batch-normalized convolutional operations (Conv-BN-ReLU), employing 3 \(\times\) 3 kernels with a stride of 1, in line with feature preservation best practices from previous research. The normalized feature maps are then concatenated with downsampled representations from preceding layers, achieved through max-pooling with a stride of 2. This creates a rich multi-scale feature space. Secondly, the framework includes a dimensionality reduction and feature enhancement component. After concatenation, the features pass through a 1 \(\times\) 1 convolutional compression layer. This is followed by processing through our novel multi-scale residual feature aggregation module (MRFA). To further optimize performance, the network incorporates multi-scale saliency attention modules following convolutional feature extraction in both encoder and decoder pathways. These modules significantly enhance global context modeling capabilities through a sophisticated attention mechanism: after independently computing attention scores for both feature modalities, a dynamically learnable weighting factor combines these scores to precisely establish token-to-token correlations. This process enables simultaneous token updating, non-local feature extraction, noise reduction, and adaptive feature reorganization to preserve the most semantically valuable information. Our framework is designed to overcome the main limitations of conventional approaches. It addresses the issue of single-scale feature dependency by using parallel  
Figure 4: The proposed model framework. multi-resolution processing streams. Finally, it tackles the problem of boundary blurring through the use of dilated convolutions and skip connections.