# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 3 Proposed Method
### Spatial Context Awareness Module  
The SDAM significantly improves the resolution of low-level features, thereby preserving critical edge definitions and textural patterns essential for detecting diminutive targets. Meanwhile, DSFM progressively integrates deep semantic information through channel enhancement and spatial attention mechanisms, thereby enhancing the model's discriminative capacity across diverse object categories. However, in complex remote sensing scenarios, relying solely on shallow detail features and deep semantic representations remains insufficient for accurately delineating target regions, particularly under conditions of intense environmental noise and substantial fluctuations in target dimensions. In such cases, the synergistic interaction between local details and global contextual information becomes essential. To address this challenge, we propose SCAM to reinforce spatial dependencies within feature maps and enhance detection robustness by leveraging both global spatial information and local perception capabilities, as illustrated in Figure 5.  
SCAM is composed of two branches: one for global spatial attention and the other for local spatial attention. Given an input feature map \(A\), we first divide it along the channel dimension to obtain two feature maps of identical dimensions, \(A_{1}\) and \(A_{2}\). In the first branch, a convolution layer is applied to extract the spatial distribution information of the input features, mapping them into a single-channel feature representation. A Softmax normalization is then performed to emphasize the attention weights of key regions. The normalized attention weights \(A_{3}\) are multiplied element-wise with the first branch's input feature \(A_{1}\) to suppress background interference while enhancing the responses in target regions. Additionally, to further improve feature representation, a multilayer perceptron (MLP) is introduced for nonlinear transformation. Lastly, a residual connection is utilized to maintain gradient stability and retain the initial feature data. The result \(A_{out1}\) from this branch is expressed as follows:  
\[A_{3} =\text{Softmax}(\text{Conv}(A_{1})) \tag{14}\] \[A_{out1} =\text{MLP}(A_{3}\otimes A_{1})\oplus A_{1} \tag{15}\]  
Figure 5: Structure of spatial context awareness module.  
where Conv refers to a 1 \(\times\) 1 convolution, MLP consists of two 1 \(\times\) 1 convolution layers, a batch normalization module followed by a ReLU activation. The transpose operation is omitted here for simplicity.  
The second branch primarily consists of a convolution layer and a depthwise separable convolution, where the former is used for feature transformation, and the latter effectively captures local spatial information. Additionally, multiple matrix operations are performed to enhance the depth of attention learning. The output of this branch \(A_{out2}\) can be as follows:  
\[A_{4} =\text{DWConv}(\text{Conv}(A_{2})) \tag{16}\] \[A_{5} =\text{Conv}\left(A_{4}\oplus A_{1}\right)\otimes A_{1}\] (17) \[A_{out2} =\text{Sigmoid}(A_{5})\oplus A_{1} \tag{18}\]  
where DWConv indicates a depthwise separable convolution with a 3 \(\times\) 3 kernel, Conv denotes a pointwise convolution with a 1 \(\times\) 1 kernel, and \(A_{4}\) and \(A_{5}\) correspond to the intermediate features of this branch.  
The global spatial attention branch captures global context information from an overall perspective, while the local spatial attention branch enhances the capability to represent features in localized regions. Finally, we fuse the two sets of features by channel concatenation to preserve both global and local information simultaneously.  
\[A_{out}=\text{Concat}(A_{out1};A_{out2}) \tag{19}\]  
where Concat(.) signifies merging along the channel axis, while \(A_{out}\) denotes the SCAM output.