Change Detection Network of Nearshore Ships for Multi-Temporal Optical Remote Sensing Images

Ship change detection is of great significance for maritime safety supervision, wharf vessel management, and vessel life cycle analysis. Nowadays, the ship change information is obtained through the difference between the multiple independent object detection results of multi-temporal images. However, it neglects the temporal correlation of the concerned features, impeding further improvement of the detection accuracy of change detection. Therefore, based on the sequential network, namely convolution LSTM, we built an end-to-end ship change detection (SCD) R-CNN network. The network extracts abstract semantic information reflecting the changed features of ships, and the time correlation between features of the different phases is established. Specifically, the changed features are utilized to guide the judgement of ship change. It is verified from the RS images that the proposed network avoids the misjudgment caused by the errors of object detection in the conventional method. In addition, a higher efficiency is revealed, maintaining the accuracy of the change detection of ship targets.