Overview of Deep Convolutional Neural Network Approaches for Satellite Remote Sensing Ship Monitoring Technology

Abstract

Remote sensing (RS) monitoring of ships has important significance in both military and civilian fields. The RS ship detection aims to locate the position of the ship in the remote sensing image and extract its characteristics. Traditional ship target detection algorithms cannot meet the demands for speed and precision of SAR remote sensing and optical remote sensing data. With the development of artificial intelligence technology, the target detection technology such as deep learning algorithms has made significant progress in RS ship detection. Deep learning has become a heated topic in research. This paper has analyzed and summarized previous researches on the application of deep learning algorithms in ship detection technologies based on SAR and optical remote sensing images in recent years and has provided suggestions for future studies. In the future, deep learning-based technologies for RS ship detection will use more data, such as data from multiple sensors in multiple channels. Deep neural networks will also include more rules and specialized knowledge. Its structure will become more complicated and eventually develop into a neural network like the human brain.