# Coupling Denoising to Detection for SAR Imagery
###### Abstract
Processing separately the large amount of SAR images results in high time consumption and low efficiency. Though various despeckling methods such as Lee filter [22], Kuan filter [23], Frost filter [24], Probabilistic Patch-Based (PPB) filter[25] have been proposed, if we take an improper despeckling methodology without considering the dataset characteristics carefully, then the despeckling may lead to poor performance due to the information loss from raw SAR images. Meanwhile, to further improve the visual quality of SAR images, there are other preprocessing methods such as contrast enhancement methods. Given that most of SAR images are usually grayscale  
Figure 1: Examples of the real-world SAR image where noise-like speckle appears. images, we can consider various processing methods, for example, fuzzy-based gray-level image contrast enhancement [31] or fuzzy-based image processing algorithm [32]. To overcome the issue and guide for directly promoting object detection performance, developing an object detection framework through incorporating an alternative deep denoiser replacing the separate denoising preprocessing step into the classical object detection network is significant and necessary. The motivation shares the similar spirit to the recent classification work proposed by [PERSON] et al. [33], where they learn a noise matrix from an input noisy image and with the noise matrix synthesize a despeckled image taken as the input into a subsequent classification network. According to our best knowledge, we are the first to connect a denoising network to an object detection network. We additionally introduce _fusing region proposals_ approach which fuses set of Region of Interests (RoIs) from both noisy and denoised images; rather than simply ending with the coupling structure as in [PERSON] et al. [33]. We propose a novel object detection framework whose the core idea comprises two parts: (1) connecting an unsupervised denoising network to an object detection network for dynamically extracting a denoised SAR image from a given noisy SAR image, and (2) forwarding an image pair of two SAR images (the given real SAR image and the synthetically generated SAR image) to an object detection network and fusing region proposals from the two SAR images for complementarily integrating regional information. Here _fusing region proposals_ refers to merging two sets of RoIs yielded by a shared region proposal network within the object detection network. This is inspired by the observation that utilizing only real SAR image may bring about false positives due to the inherent speckle noise of the image and on the contrary, depending on only denoised SAR image may cause missing targets because inadequate denoising leads to fine information loss of raw data. The rest of this paper is organized as follows. Section 2 mainly consists of two parts, where the first part introduces our datasets constructed with SAR images from TerraSAR-X and COSMO-SkyMed satellites, and the second part describes the detailed design of our proposed object detection framework, i.e., how to incorporate an unsupervised denoising network into an object detection network and fuse the region proposals within the object detection network. Section 3 reports comparative experimental results for the proposed object detection network on our own datasets. To validate the effectiveness of our approach, we carry out multiple experiments; (1) we need to experimentally demonstrate that our coupling structure between denoising and detection networks can strengthen detection performance, (2) we further verify the proposed region proposal fusing strategy in terms of input data for detection network and fusing method through ablation studies, and (3) we additionally perform comparative experiments with respect to the choice of a feature map extracted from either real or synthetic SAR image, where the feature map refers to the output of CNN backbone in the detection network.