Robust PCANet on target recognition via the UUV optical vision system

Abstract

Inspired by the importance of preserve the robustness of algorithm, in this paper, a simple and effective underwater target recognition algorithm called robust principal component analysis network (RPCANet) is established, which has further enhanced the operational ability of unmanned underwater vehicle (UUV) with optical vision systems. Several advantages of RPCANet are summarized as follows. First, RPCANet is superior to traditional underwater target recognition methods, such as principal component analysis (PCA), because it is not sensitive to severe outliers. Second, RPCANet employs ....Fp.... -norm as the distance metric for obtaining a filter. The proposed method retains the desirable properties of the PCA, which includes a solution related to the covariance matrix and rotational invariance. In addition, RPCANet is a simple deep learning network used for image recognition and classification. Finally, the performance of the proposed method is illustrated by video image data collected by the optical vision system of the UUV. The experimental results show the advantages and effectiveness of proposed recognition scheme.