Sentinel-2 Research on the Detection and Classification Methods of Maritime Ship Targets from Remote Sensing Images

• Published in: Journal of Physics: Conference Series
• Main Authors: He, Junjie, Lin, Yinan, Shi, Fangzhe, Fu, Jiajun, Chen, Boning
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2023
• Subjects: Attu
• Online Access: http://dx.doi.org/10.1088/1742-6596/2425/1/012014; https://iopscience.iop.org/article/10.1088/1742-6596/2425/1/012014; https://iopscience.iop.org/article/10.1088/1742-6596/2425/1/012014/pdf

Abstract There are problems such as low recognition accuracy and large classification error in the existing classification methods for ship identification based on optical remote sensing images. In this paper, we will analyze the characteristics of ships and determine the indicative factors for applying remote sensing to monitor ships in combination with optical remote sensing images. Using optical remote sensing image data, combined with U-Net and AttU-Net deep neural network models, we assist in extracting new remote sensing indices with strong generality and clear physical meaning, and establishing rules for monitoring ships, so as to establish a more general and clear physical meaning of the monitoring and identification method of remote sensing satellite images. The method is applied and evaluated with port optical remote sensing image data. The data show that compared with traditional machine learning methods, the accuracy of ship monitoring using U-Net and AttU-Net deep learning models in this paper reaches 89.04%, and the recall rate and accuracy rate are better than SVM. it shows that the model can detect ships effectively.