A ship target automatic recognition method for sub-meter remote sensing images
Shuai Tong, Kang Sun, Benhui Shi, Jinyong Chen · 2016
The spatial resolution is increasingly high as development of optical remote sensing, and more and more optical sensors can achieve the detection ability of sub meter, which lays down the data foundation for automatic recognition of ship targets. However, mature technology is lacked to identify the ship models automatically with remote sensing images. In this study, an automatic recognition method for ship targets is proposed based on the local invariant feature extraction algorithm SIFT (Scale Invariant Feature Transform), which is consist of feature extraction and description, feature matching and target recognition. The model of unknown target is identified based on the target library using the matching difference of targets with the same model and different models. The experiment results show that this automatic recognition flow is effective to identify the ship targets of interest based on the target library, and the total correct recognition rate is 92%. This method provides a new flow for automatic model recognition of ship targets, and has considerable potential for wide applications.