Automatic identification of docks can provide an important basis for the construction and development of ports, acquisition of coastal geographic information, and evaluation of maritime military strength. However, docks are characterized by small sizes, large quantities, and scattered distribution. Docks are also negatively affected by serious information interference of the surrounding environment, including ships and buildings. Traditional algorithms cannot easily meet the needs of accurate monitoring of rapidly developing docks. Accurate identification of dock targets has become an urgent problem to be solved. Based on the open remote sensing data sets and Google Earth high-resolution remote sensing images, the data sets of three types of docks are constructed, and the following improvements are made to the Faster R-CNN algorithm according to the size and spatial distribution characteristics of docks. (1) The K-means algorithm is used to preset the anchors, making the anchor sizes more suitable for the actual dock sizes. (2) Soft-NMS is used instead of NMS to reduce the rates of mistaken deletion and missed detection of dock borders in densely distributed areas. The experimental results show that the accuracy of the improved FKSN algorithm reached 92.6%, which is 6% higher than that of the Faster R-CNN algorithm. The final result of dock target recognition is compared with the ones of the traditional classification methods such as ISODATA, SSD, Faster R-CNN, and Faster R-CNN+K-Means. Among these approaches, the method suggested in this paper performs best in the evaluation indices of false alarm rate and omission rate, which are 3.2% and 7.6%, respectively. Thus, the proposed method has a better effect on the identification of various dock targets. The automatic dock identification algorithm based on the improved Faster R-CNN can provide technical support for reasonable construction, planning, and management of docks and provide effective approaches for efficient utilization and military strength analysis of docks.

Faster R-CNN;automatic dock identification;K-Means algorithm;Soft-NMS algorithm;high resolution remote sensing