# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 1 Introduction  
Optical remote sensing image data processing technology, represented by ship target detection and recognition at sea, has become the core key to the current intelligent application of space-earth integration and has been widely used in fishery management, maritime rescue, and regional monitoring [1]. For a long time, the real-time detection of ocean-going ship targets has mainly relied on high-resolution remote sensing satellites. Still, problems include too many mission requirements, significant differences in imaging position and time, and complex satellite mission planning [2]. In addition, the imaging process is interfered with by night and day, clouds, morning and evening light and dark, and some real-time observation requirements are challenging to meet [3]. Several civil remote-sensing small satellites have emerged in the past five years, effectively filling the gap in imaging application tasks [4]. Compared with the large platform high-resolution series satellites, the imaging resolution of small satellites is low, the average definition could be higher, and the platform stability performance could be better, which leads to the low quality of the original image products produced [5]. However,considering the time-space coverage and application requirements in emergencies, remote sensing images of low-resolution wide-area can effectively display the surrounding state of the target, and sequence images can show the motion information of the target itself, which has attracted wide attention in real-time application scenarios [6]. In a meter-level/10-m-level resolution image, the target may only account for about 10 pixels in the picture. Small and medium-sized ships present a wedge-shaped Gaussian gray distribution, and the details of the target texture disappear, and only the exterior outline of the hull is retained. Therefore, it is difficult to confirm whether the target is a ship from the target's details and surrounding context [7]. The ship targets in the image are tiny and densely distributed, characterized by small size, large number, diverse locations, and extensive background interference, which are pretty different from the large and prominent detection targets in the public data set [8]. In the low-resolution scenario, the visual sea state tends to be flat, and the trawl traces of ships are difficult to identify. However, the similar hull shape formed by broken clouds significantly reduces false alarms [9]. The image sequence can eliminate false alarms according to the motion information. Still, it needs to consider how to deal with the target association between the low-confidence frames under the sparse registration information of the before and after and combine the motion characteristics of the target to form the trajectory information. Although some studies use GAN Network (Generative Adversarial Network) to carry out super-resolution for small remote sensing ships [10], this method could be more practical in low-resolution images with low contrast. Currently, remote sensing ship target detection mainly relies on public data sets based on Google Earth, which are primarily oriented to acceptable recognition applications and difficult to use in low-resolution scene applications [11]. Previous high-resolution remote sensing ship target detection algorithms mainly focused on recognition and classification based on machine learning and deep learning. Low-resolution data are challenging to form accurate feature or texture detail training data sets. At the same time, the statistical characteristics of the target and false alarms are consistent, which makes it difficult to eliminate some false alarms by accumulating data [12]. In reference [13], a multi-frame sequence registration method is designed. The target speed is set to be constant, and the motion information is obtained according to the linear correlation of the target trajectory of each frame. This method sets the target to move linearly, which requires high registration accuracy.