## 2 Methods and Data
### New Algorithmic Ateps
#### 2.2.2 LSK Module  
In remote sensing image processing, most target detectors often result in classification errors due to the limited context information. Moreover, there may be significant differences in the amount of context information required to detect different remote sensing objects, which may decrease the model's accuracy. Although the CNNS has achieved great success in computer vision tasks such as image classification, object detection, and semantic segmentation, traditional convolutional layers struggle to adequately capture objects of different scales and shapes due to their fixed-size receptive fields. Although large nuclear convolution can extend the receptive field, it is computationally expensive and ineffective when dealing with small targets. To better adapt to the needs of multi-scale target feature extraction and improve the precision of the resulting model, we use an LSK module [31], which has a flexible receptive field, enhanced spatial selectivity, rich feature expression, and computational efficiency. Through multi-scale feature fusion, the receptive field size can be dynamically adjusted, which makes the model more flexible when dealing with various scale targets. The generated spatial selection mask enables the model to focus on the most important features at specific spatial locations, thus improving the accuracy of target detection. The information interaction and fusion of multi-scale features improve the richness of feature expression and enhance the discriminant ability of the network. In addition, the LSK module achieves higher computational efficiency by splitting the convolutional kernel and the attention mechanism compared to the direct use of the large convolutional kernel. The LSK module is a new structure for CNN, as shown in Figure 3. It aims to significantly improve the feature extraction and target detection capability of the model in complex scenes through multi-scale feature fusion and selective spatial attention mechanisms. The LSK module enhances the flexibility and discriminative ability of feature expression through the multiscale convolutional kernel and selective spatial attention mechanism. First, the LSK module extracts multi-scale features through convolution terms of different sizes (e.g., 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7), which capture contextual information of different scales to form preliminary multi-scale feature maps. This process can be expressed as:  
\[\text{F}_{\text{s}}=\{\text{f}_{3\times 3}(\text{X}),\text{f}_{5\times 5}(\text{X }),\text{f}_{7\times 7}(\text{X})\} \tag{4}\]  
where \(\text{F}_{\text{s}}\) represents a preliminary set of multi-scale feature graphs, and \(\text{f}_{\text{k}\times\text{k}}(\text{x})\) represents the convolution operation of a convolution kernel of size \(\text{k}\times\text{k}\) on input x. Channel average pooling and maximum pooling operations are applied on each feature map to obtain the average and maximum response of the channel, respectively. These two pooling operations can effectively summarize and highlight the spatial relationship in the feature map, and the process can be expressed as follows:  
\[\mathrm{F_{avg}=AvgPool(F_{s}),F_{max}=MaxPool(F_{s})} \tag{5}\]  
where \(\mathrm{F_{avg}}\) and \(\mathrm{F_{max}}\) represent the feature maps after channel average pooling and Max pooling, respectively. Then, pooling features at different scales are fused through a convolutional layer to generate multiple spatial attention maps. This process can be regarded as the integration of information at different scales to obtain a more comprehensive feature representation. This fusion process can be expressed as follows:  
\[\mathrm{F_{att}=\sigma\Big{(}W\Big{(}F_{avg}\bigoplus F_{max}\Big{)}\Big{)}} \tag{6}\]  
where \(\mathrm{F_{att}}\) represents the generated spatial attention map, \(W\) represents the weights of the convolutional layer, and \(\sigma\) represents the number of sigmoid activation functions.