# Coupling Denoising to Detection for SAR Imagery
###### Abstract  
Detecting objects in synthetic aperture radar (SAR) imagery has received much attention in recent years since SAR can operate in all-weather and day-and-night conditions. Due to the prosperity and development of convolutional neural networks (CNNs), many previous methodologies have been proposed for SAR object detection. In spite of the advance, existing detection networks still have limitations in boosting detection performance because of inherently noisy characteristics in SAR imagery; hence, separate preprocessing step such as denoising (despeckling) is required before utilizing the SAR images for deep learning. However, inappropriate denoising techniques might cause detailed information loss and even proper denoising methods does not always guarantee performance improvement. In this paper, we therefore propose a novel object detection framework that combines unsupervised denoising network into traditional two-stage detection network and leverages a strategy for fusing region proposals extracted from both raw SAR image and synthetically denoised SAR image. Extensive experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from TerraSAR-X and COSMO-SkyMed satellites. Extensive experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from TerraSAR-X and COSMO-SkyMed satellites. The proposed framework shows better performances when we compared the model with using only noisy SAR images and only denoised SAR images after despeckling under multiple backbone networks. capaiseGreen  
applications such as information and electronic warfare, target recognition of aircrafts that maneuver irregularly, battlefield situational awareness, and development of aircrafts that are hard for the other party to track with radar. In addition, it is necessary to study on object detection using radar imagery for civilian applications (e.g., resources exploration, environmental monitoring, etc.). With the recent rapid development of deep learning, many deep convolutional neural network (CNN)-based object detection approaches using SAR imagery have gained increased attention. The successes of the deep detectors on SAR images facilitate a wide range of civil and military applications, such as detection of ship [1; 2; 3; 4; 5], aircraft [6; 7; 8; 9], destroyed building [10], oceanic internal wave [11], oceanic eddy [12], oil spill [13], avalanche [14], and trough [15]. For the further research purposes, several SAR object detection datasets have also been released called AIR-SARShip-1.0 [16], SAR-Ship-Dataset [17], SAR ship detection dataset (SSDD) [18], and HRSID [19]. SAR images are formed from a coherent sum of backscattered signal components at the boundary of different media after pulsed transmissions of microwave radiation, enabling to observe the interior of the targets otherwise invisible to the naked eye. However, when obtaining the SAR images, if the emitted pulses are reflected from the boundary of a target with uneven surface, then scattering and interference waves are created. These wave signals have a direct impact on a SAR imaging the structure of the target as noise components. The produced noise is often called _speckle noise_, which hinders the original image information and causes a speckle corrupted SAR image as shown in Figure 1. The scattering characterization of the target gets severe depending on changes in radial properties and orbital surfaces, leading to degradation of recognition performance. It is worth noting that a number of published studies were conducted for denoising (or despcekling) SAR images [20; 21; 22; 23; 24; 25]. Many previous works first perform despeckling on SAR images as one of preprocessing steps and then utilize the SAR images for several tasks via deep learning; e.g., classification task [26; 27], detection task [28; 29; 30], etc.