# Coupling Denoising to Detection for SAR Imagery
## 2 Materials and Methods
### Proposed Methodology  
Given the inherent speckle noise of SAR, researchers have previously performed a preprocessing step like despeckling before training an object detection model. However, such prior preprocessing independent of the performance of object detection may not only be inefficient, but also lead to weak detection performance because an unintentionally improper denoising induces loss of detailed information. Therefore, we integrate a denoising network with a two-stage detection network so that the denoising network can directly receive feedbacks from the detection network, as illustrated in Figure 6.  
We choose a blind-spot neural network [36] based self-supervised scheme as the unsupervised denoising model and adopt Gamma noise modeling as in Speckle2 Void [37] fitted with SAR speckle, but not limited to this model sturcture. We can train the unsupervised denoising model as a generator \(G\) that maps a real (noisy) SAR image \(I_{real}\) to the synthetic (denoised) SAR image \(G(I_{real})\). The core idea of our model is to infer a synthetic denoised SAR image from the input SAR image and merge the two sets of extracted RoIs to improve detection performance. Without any help of related materials such as corresponding denoised image for an input SAR image, we can autonomously simulate the denoised image and fuse the inferred information such as RoIs. The entire model enables effective end-to-end learning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\# Instances** & **\# Patches** & **\# Areas** & **Patch Size** & **Resolution** \\ \hline AIR-SARShip-1.0 [16] & 461 & 31 & 4 & 3000 \(\times\) 3000 & 1\(\sim\)3 m \\ SSDD [18] & 2540 & 1160 & 15 & 300 \(\times\) 400 & 1\(\sim\)10 m \\ SAR-Ship-Dataset [17] & 59,535 & 43,819 & 30 & 256 \(\times\) 256 & 3\(\sim\)25 m \\ HRSID [19] & 16,951 & 5604 & 13 & 800 \(\times\) 800 & 0.6\(\sim\)3 m \\ Our Dataset & 21,717 & 16,308 & 92 & 800 \(\times\) 800 & 0.6\(\sim\)1 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of statistics among multiple datasets. We denote the number of instances, patches, and areas as # Instances, # Patches, and # Areas, respectively. Figure 5: Histogram that exhibits the number of annotated instances with respect to area (width \(\times\) height) in pixels. The unsupervised denoising network \(G\) in our model firstly takes as input a real (noisy) SAR image \(I_{real}\) and extracts synthetic (denoised) SAR image \(G(I_{real})\) as the output. Then, the formed (real, synthetic) image pairs \((I_{real},G(I_{real}))\) are fed into a shared region proposal network and the region proposal network outputs two corresponding feature maps and sets of RoIs. The two sets of RoIs \(\mathbb{B}_{real},\mathbb{B}_{synth}\) are merged and the redundant bounding boxes are subsequently removed by a NMS procedure, i.e., \(\mathbb{B}_{final}=NMS(\mathbb{B}_{real}\cup\mathbb{B}_{synth})\), where \(\mathbb{B}_{final}\) is the resultant fused bounding boxes. For each RoI in \(\mathbb{B}_{final}\) on the feature map from the real SAR image, the RoI feature vector is then forwarded to obtain the classification and regression results as traditional two-stage detection network. Usually, only single SAR image which is either real or denoised (preprocessed) is employed for training an object detection network as shown in Figure 7. Suppose we have real SAR images which is inherently speckled noisy without any preprocessing, relying solely on the real SAR image for training may cause false alarms of region proposals. On the other hand, utilizing denoised SAR images alone may be prone to suffer from missing targets because of detailed information loss. We, therefore, devise a novel denoising-based object detection network to make full use of the complementary advantages between the real and denoised SAR images. To combine extracted information from both real and synthetic SAR images, we consider _fusing region proposals_ which merges two sets of RoIs yielded by a region proposal network.