# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 3 Methods
### Hybrid Attention Mechanism  
Pixel attention and channel attention can both emphasize informative features and prevent small objects being overwhelmed by noises. Hence, this paper proposes a hybrid attention mechanism (HAM) integrating pixel attention and channel attention. Pixel attention uses a supervised network to enhance the object-related pixel features and retain context information, which can learn more accurate location information. Channel attention uses SKNet [30], which can consider the relationship between channels, as well as the convolutional kernels, allowing the network to focus on high quality channels.  
The HAM module is shown in Figure 4. In the pixel attention network, the feature map \(C\) first passes through an inception structure with different ratio convolutional kernels to learn a two-channel saliency map. The saliency map indicates the scores of the foreground and background, respectively. To guide this network, a supervised learning approach is used. The network obtains a binary map as the label according to ground-truth, and sets the object pixels in ground-truth to 1, while the rest of the pixels are set to the background value of 0. Then, the network uses the cross-entropy loss of the binary map and the saliency map as the attention loss:  
\[L_{atten}=-\frac{1}{w\times h}\sum_{xy}\left(l_{xy}\log(l_{xy})+(1-\hat{l}_{ xy})\log(1-l_{xy})\right) \tag{4}\]  
where \(w\) and \(h\) are the width and height of the saliency map, while \(\hat{l}_{xy}\) and \(l_{xy}\) are the ground-truth values and saliency map values of the pixel points at \((x,y)\), respectively.  
Meanwhile, as the saliency map is continuous, background information will not be completely eliminated, which facilitates the retention of context information and improves robustness. Then, a softmax operation is performed on the saliency map and the weight matrix of pixel attention is obtained.  
Channel attention uses SKNet, which contains three parts, namely split, fuse, and select. Split generates two feature maps containing different scale information by different convolutional kernels. Fuse combines two feature maps from different branches through element-wise addition, integrating information from different branches. Then, the global information is embedded through global average pooling (GAP), which is squeezed to a 1 \(\times\) 1 \(\times\) C vector representing the channel-based statistics, and the vector is followed by a fully connected (FC). Select regresses the weight information between the channels by using two softmax functions, and the weight information is multiplied by feature maps generated in split. Finally, the summed vector is output, representing the channel weight.  
HAM multiplies pixel attention weights and channel attention weights, and obtains a new feature map \(A\). Unlike channel attention that only focuses on the importance of  
Figure 4: Hybrid attention mechanism.  
different channels, HAM also considers encoding the spatial information. In detail, pixel attention weight can increase the importance of object location, effectively enhancing object location features and weakening background features. Meanwhile, channel attention weight can increase the importance of task-related channels, allowing the network to selectively emphasize object-related features and suppress useless ones. Thus, the HAM module can learn more object features to weaken the background noise and to prevent the misdetection of small objects.