Contour Refinement and EG-GHT-Based Inshore Ship Detection in Optical Remote Sensing Image

Inshore ship detection becomes challenging in high-resolution optical remote sensing image (RSI) because inshore ships are often incomplete and deformed due to the poor imaging condition and shadow of ship superstructure, and there are various interferences in harbor. A contour refinement and the improved generalized Hough transform (GHT)-based inshore ship detection scheme is proposed for RSI with complex harbor scenes. First, the suspected region of ships (SRS) is located in the entire RSI according to the line segments of ship body and docks. The contours in each SRS are then refined to repair the damaged ship head contour (SHC) using the convex set characteristics of ship head and subsequently reduce non-SHC by curvature filtering. In each refined SRS, equal frequency quantification instead of equal width quantification for R-Table construction and Gini coefficient-based decision criterion combining the number and distribution of votes are proposed to improve GHT (i.e., EG-GHT) and to extract SHCs as candidate targets. The false candidates are removed according to pixel proportion described by the structured binarization feature. Applying the border scoring strategy, the best candidates with the largest score among all the overlapped bounding boxes are selected as the final detection targets. Using the public RSIs with various cases, including turbid water, cloud occlusion, ships moored together, and ships with the different sizes, experimental results demonstrate the proposed scheme outperforms state-of-the-art contour-based methods and deep learning-based methods in terms of precision-recall rate and average precision, respectively.