A Novel Ship Target Detection Algorithm Based on Error Self-adjustment Extreme Learning Machine and Cascade Classifier

Abstract

High-frequency surface wave radar (HFSWR) has a vital civilian and military significance for ship target detection and tracking because of its wide visual field and large sea area coverage. However, most of the existing ship target detection methods of HFSWR face two main difficulties: (1) the received radar signals are strongly polluted by clutter and noises, and (2) it is difficult to detect ship targets in real-time due to high computational complexity. This paper presents a ship target detection algorithm to overcome the problems above by using a two-stage cascade classification structure. Firstly, to quickly obtain the target candidate regions, a simple gray-scale feature and a linear classifier were applied. Then, a new error self-adjustment extreme learning machine (ES-ELM) with Haar-like input features was adopted to further identify the target precisely in each candidate region. The proposed ES-ELM includes two parts: initialization part and updating part. In the former stage, the L1 regularizer process is adopted to find the sparse solution of output weights, to prune the useless neural nodes and to obtain the optimal number of hidden neurons. Also, to ensure an excellent generalization performance by the network, in the latter stage, the parameters of hidden layer are updated through several iterations using L2 regularizer process with pulled back error matrix. This process yields appropriate output weights and the appropriate hidden weights. Experimental results show that (1) compared with standard ELM, our proposed ES-ELM has higher classification accuracy and training efficiency, and the generalization performance is not sensitive to regularization parameter, (2) the proposed ship target detection algorithm based on ES-ELM outperforms most of the state-of-the-art methods for detection accuracy and computational efficiency.