# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 1 Introduction  
Unlike optical satellites, synthetic aperture radar (SAR) satellites work in all weather conditions thanks to active data collection. They are widely used in marine monitoring and are the main remote sensing means for large-scale maritime ship target detection. Marine ship target detection can monitor maritime traffic, maintain maritime rights and interests, and improve the early warning capabilities of coastal defenses [1, 2, 3]. Therefore, high precision positioning of ship targets is required. The traditional image information acquisition process involves four main steps: data acquisition, data download, ground centralized processing, and information extraction. This process can be slow, and the longer the time from when the satellite generates the data to when information is extracted on the ground, the less useful that data will be. It is necessary to migrate the processing and information extraction algorithms from the ground to the on-board computing platform in order to make full use of the limited transmission bandwidth and satellite transmission time to shorten the time delay in information acquisition. This migration would also reduce the load on the satellite-ground data transmission system [4, 5, 6]. Many satellites have the capability of on-board processing of remote sensing data. The Earth Observation-1 launched by NASA's New Millennium Program in November of 2000 has on-board processing capabilities that include emergency monitoring, feature monitoring, change monitoring, and anomaly monitoring. In October 2001, DigitalGlobe launched the QuickBird satellite, which was the highest resolution commercial satellite to date and it can produce multiple types of images and perform remote sensing image preprocessing and real-time multispectral classification on-board. The NEMO satellite, launched by the US, is equipped with COIS coast imaging spectrometer and adaptive spectrum identification system ORASIS and it can generate and directly download coastal description information [7]. On-board processing is usually implemented by Digital Signal Processor and Field-Programmable Gate Array; however, due to their poor scalability, implementing new algorithms is difficult, which restricts them from meeting the diverse needs of intelligent on-board processing. However, with rapid advancements in computing hardware, such as the advent of embedded Graphic Processing Units (GPUs) with low power consumption, strong performance, and high integration, new solutions for on-board real-time processing have been made available [8], such as NVIDIA Jetson TX1 and TX2. In the past few years, researchers have proposed many traditional ship detection methods utilizing SAR imagery [9; 10; 11; 12]. [PERSON] et al. [9] proposed an adaptive and fast CFAR algorithm that was based on automatic censoring (AC), and the combination of AC with the algorithm resulted in good performance. [PERSON] et al. [10] used a new ship detection approach based on multi-scale heterogeneities under the a contrario decision framework, this method proved to be suitable for ships of different sizes. [PERSON] et al. [11] used the K-distribution CFAR method to calculate a global threshold with a given false-alarm rate, then mathematical morphological filters are applied to the results; this approach greatly improved the processing speed. [PERSON] et al. [12] proposed an algorithm for ship target gray distributions based on correlations, and then the joint gray distribution models of pixels and adjacent pixels in clutter are established using the two-dimensional joint lognormal distribution. However, these methods are dependent on the pre-classification and pre-definition of features by humans, and they may be less robust in complex backgrounds. Furthermore, no on-board traditional method for real-time SAR processing has been verified. With the development of computer vision, deep learning has gradually been applied to ship detection from SAR imagery. Target detectors that are based on deep learning can be divided into two categories.