Fast on-board ship detection in panchromatic images based on micro- nano satellite

Abstract

With the continuous improvement of the number and capability of micro-nano satellites, on-board intelligent data processing becomes a necessary configuration. The constellation with hundreds micro-nano satellites has the ability to high-frequency detection of ship targets and realizes continuous awareness of the global ocean, which is of great significance in maritime rescue, waterway management and combating illegal fishing. In this paper, a fast on-board ship detection method for panchromatic image is proposed. Firstly, GPU (graphics processing unit) of commercial devices is used to form high performance and low power computing capability on the micro-nano satellite. Then, according to the characteristics of ship targets, a convolutional neural network based on lightweight model is designed to quickly obtain accurate number and location information of ship targets. The algorithm deployed on micro-nano satellite can transform massive remote sensing data into target slices, greatly reduce the pressure of satellite-ground data transmission and improve the application efficiency of remote sensing data. We test our method on a dataset of more than 90 panchromatic images. The results show that the detection rate of this algorithm is better than 0.95, and the average processing speed for an image block of 1024 × 1024 pixel is less than 0.2 seconds, which has a wide application prospect.