# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 1 Introduction  
Owing to the characteristics of wide coverage, long duration, remote access, and high data collection volume, earth observation satellites play a key role in urban planning, traffic surveillance, geological exploration, disaster assessment, military reconnaissance, etc. Among these satellites, micro-nano satellites have the specific advantages of small size, low power consumption, short development cycle, suitable for networking and constellation, and low cost to complete many complex space missions. Therefore, micro-nano satellites have become the research hotspot in the fields of scientific research, national defense construction, and commercial application [1, 2]. Global Maritime Domain Awareness (MDA) [3] is one of the important applications of micro-nano satellites, and the periodic full coverage data of micro-nano satellites form the data basis for the extraction of target information in a large area. Ships are the main carrier of maritime transport, therefore the accurate position of ship target is of great significance and value for MDA. The traditional processing flow of ship detection from remote sensing images consists of mission planning, satellite imaging, satellite-earth data transmission, and ground processing. The ground processing scheme has time delay for ground users, and the satellite-earth data transmission system is under tremendous pressure. There are two bottlenecks for the further development of micro-nano satellites, i.e., the hysteresis of satellite-ground mutual communication of massive remote sensing images and the low efficiency of traditional information processing. On-board processing in micro-nano satellites is an effective way to improve the response speed to emergencies and provide immediate products for users. Figure 1 shows the comparison between on-board processing flow and the traditional ground processing flow. On-board image processing analyzes the remote sensing images directly on the satellite, and then only transmits the effective information to the ground receiver immediately. Compared with the traditional ground processing flow of ship detection, the advantages of on-board processing flow of ship detection are as follows: (1) the gigabyte level images are simplified to megabyte level target patches and information by on-board ship detection. It can reduce the compression, transmission and storage pressure for emergencies. (2) It simplifies the process of ground equipment and directly delivers the concerned information to users from the satellite. (3) Users can acquire information in near real-time while traditional processing flow needs several hours or days. The core of on-board processing is the hardware architecture and the intelligent processing algorithm. Current on-board processing mainly applies the architecture of Field-Programmable Gate Array (FPGA) and Digital Signal Processor (DSP). Such architecture has limited computing capacity and the existing algorithms on the hardware architecture can only complete some simple tasks, such as image compression [4], ortho-rectification [5], point matching [6], and preprocessing [7]. With perceptually lossless compression methods [8], the satellites may have the ability to downlink more images to ground. However, the volume of compressed images is still too large for limited downlinking bandwidth even under high compression ratio, especially for urgent tasks and micro-nano platforms. As for the intelligent processing algorithm, there are two main problems for ship detection on micro-nano satellite. One is on how to use the limited computing resources on the satellite to detect ships from massive images. The on-board resources are severely limited (configuration, power consumption, computing capability, etc.) due to the small size and weight of micro-nano satellites. The other one is on how to efficiently detect ship targets in a large area under complex scenes (cloud, fog, wave, island interference, etc.).