A Two-Stage Hierarchical One-Class Classification Structure for HFSWR Ship-Target Detection
Wandong Zhang, Yimin Yang, Tianlong Liu, Q. M. Jonathan Wu
Abstract
A high-frequency surface wave radar (HFSWR) is an effective tool for monitoring an exclusive economic zone (EEZ). However, the presence of diverse clutters and noises that contaminate the echo signals of the radar hinder its maritime surveillance. To address this issue, this paper presents a two-stage hierarchical one-class classification network (HOCN) designed specifically for ship-target detection in range-Doppler (RD) images. In Stage 1, the plausible region of interest (PROI) is extracted. This stage employs a dynamic threshold optimization strategy and Laplacian kernel to identify the potential regions of interest. In Stage 2, the proposed one-class deconvolutional-and-convolutional network (OC-DCNet) is utilized for fine detection of ship-targets. This stage comprises two sub-modules: the deconvolutional sub-module, which expands the input into a 2D matrix, and the convolutional sub-module, which classifies the input pattern as either a ship-target or a non-ship-target. The experimental results on a newly collected dataset called HFRD demonstrate the effectiveness of the proposed HFSWR ship-target detection algorithm.