# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 3 Proposed Method
### Shallow Detail Aggregation Module  
Shallow feature details are crucial in remote sensing object detection. However, while shallow features contain rich edge and texture information, they are also highly susceptible to noise interference. Therefore, a well-designed fusion mechanism is needed to enhance discriminative features while suppressing background distractions. Mamba [35], an efficient sequence modeling approach based on state space models (SSM), captures long-range dependencies without explicitly computing global self-attention, providing benefits over conventional Transformer [36] models by enhancing computational efficiency and improving global perception of shallow features. Therefore, we propose the shallow detail aggregation module (SDAM), which integrates the Mamba structure with channel-spatial attention mechanisms, enabling efficient modeling of global dependencies in shallow features while enhancing key region representation. As illustrated in Figure 3, two feature maps \(X_{1}\in\mathbb{R}^{C\times H\times W}\) and \(X_{2}\in\mathbb{R}^{C\times H\times W}\) of the same dimensions are first concatenated along the channel dimension, followed by a \(1\times 1\) convolution for feature compression, obtaining the compressed feature representation X. Based on VMamba [37], a linear projection is utilized to increase the feature channels to \(\lambda\)C (where \(\lambda\) represents a predefined expansion factor), then processed through depth-wise convolution, SiLU activation, 2D-SSM processing, and layer normalization (LN). The process can be as follows:  
\[X =\text{Conv}(\text{Concat}(X_{1},X_{2})) \tag{1}\] \[X_{4} =\text{LN}(\text{2D-SSM}(\text{SiLU}(\text{DWConv}(\text{Linear} (X))))) \tag{2}\]  
where Conv refers to a \(1\times 1\) convolution, while DWConv signifies depthwise convolution, and 2D-SSM is a 2D selective scanning module. Figure 2: The overall framework of ProSAF-Net. SDAM represents the shallow detail aggregation module, DSFM denotes the deep semantic fusion module, and SCAM refers to the spatial context awareness module. To further enhance feature selection capability, the second branch processes \(X_{3}\) (the initial concatenated feature map) through channel-based attention and spatial-based attention mechanisms, thereby improving the network's ability to focus on key object regions. CA computes channel-wise importance weights using global average pooling (GAP) and an MLP transformation, strengthening essential channel features. SA extracts spatial features through max-pool and average-pool, producing an attention map that directs the network to emphasize relevant regions. This process can be as follows:  
\[X_{5}=\text{SA}(\text{CA}(X_{3})) \tag{3}\]  
where CA denotes channel attention, and SA represents spatial attention. Finally, The Hadamard product is utilized to fuse features from both branches, followed by a linear projection to restore the original channel dimensions 2C. The process can be as follows:  
\[X_{6} =\text{Linear}(X_{4}\odot X_{5}) \tag{4}\] \[X_{out} =\text{Conv}(X_{6}\oplus X_{3}) \tag{5}\]  
where \(\odot\) represents the Hadamard product, and Conv denotes a \(1\times 1\) convolution. Unlike mainstream feature fusion structures, such as FPN, BiFPN, and PANet, which primarily focus on top-down or bidirectional propagation of multi-scale features, the proposed shallow detail aggregation module (SDAM) emphasizes the preservation and refinement of shallow visual details. Its architecture is inspired by a lightweight encoder-decoder structure, specifically tailored to enhance small object detection in remote sensing images. This design inherently supports multi-scale feature fusion: the encoder progressively extracts semantic representations, while the decoder gradually restores spatial resolution through upsampling operations, thereby retaining key positional cues and boundary information of small objects.