Using Clean Energy Satellites to Interpret Imagery: A Satellite IoT Oriented Lightweight Object Detection Framework for SAR Ship Detection  
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1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 1  
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5 [PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 1  
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2 University of Chinese Academy of Sciences, Beijing 100094, China; 23 [PERSON]L_ADDRESS]  
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1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 12342334  
been widely applied to marine monitoring due to their self-illumination capability [4]. Ship object detection is commonly used in port surveillance, illegal smuggling surveillance, and military use. Traditionally, remote sensing data must be transferred to the ground station and processed through a time/computing-consuming intelligent interpretation algorithm to detect the ship object. However, with the vigorous development of the commercial aerospace industry, the cost of entering space has been dramatically reduced. Low Earth Orbit (LEO) satellite launch increased significantly [5]. Satellite remote sensing has entered the era of a big data link with IoT. In this trend, the traditional remote sensing object detection route is outdated. It limits the interconnection between satellites and other ground sensors such as GIS, search and rescue, autopilot, etc. Furthermore, processing large amounts of remote sensing data at ground stations also brings a lot of energy consumption. Transplanting the algorithm to satellites for on-orbit interpretation has three advantages: The first is for satellites. Due to their global positioning and ultra-long distance communication, they are particularly essential when sensors and actuators are located in remote areas without service from terrestrial access networks [6]. The on-board interpretation of the object information can be directly transmitted to other sensors through the space-based IoT through the inter-satellite and satellite-to-ground links, which has more advantages in terms of timeliness and coverage. The second point is to transmit the data after on-orbit interpretation through the space-based IoT, which can realize data enhancement of remote sensing information in many aspects. Take MIMO-SAR [7; 8; 9] as an example: The SAR performance is limited by target scintillation; by exploiting the diversity of target scattering, MIMO technology applied to SARs can significantly improve resolution and sensitivity, and detection and estimation performance for IoT space applications [10]. The third is that due to the satellite being powered by solar energy, the energy consumed by the interpretation of a large number of remote sensing images does not impose an additional burden on the ground stations, which is more environmentally friendly. A breakthrough in Convolutional Neural Network (CNN) Technology [11] shows excellent performance in the computer vision field. Many state-of-the-art CNN-based object detectors [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] show superior performance, but at the same time, the high-performance computing (HPC) methods, also correspond to high energy consumption. On-orbit HPC is difficult; satellites are powered by solar arrays (SA), and the area of the SA decided the power supply for the satellite platform. However, due to the high manufacturing cost of solar panels and the space constraints of the launch vehicle fairing, the area of the SA is limited, so the power consumption of the satellite is also limited. Furthermore, the particle radiation of the space environment spared the design of the electronic components, and the computing power of onboard satellite computers was also limited. Some existing intelligent target recognition algorithms are directly transplanted to the satellite platform and cannot run smoothly and stably. The most cost-effective way to solve the above problems is to optimize the detection algorithm.