Weighted Fusion-Based Representation Classifiers for Marine Floating Raft Detection of SAR Images

Abstract

Detection of a marine floating raft is significant for ocean utilization, which provides a basis for marine ecosystem protection. In this case study, supervised classifiers of weighted fusion-based representation are proposed to detect marine floating raft using synthetic aperture radar images. To remove the speckle noise and obtain more discriminative features, a weighted low-rank matrix factorization (WLRMF) model is developed to optimize features before detection, where the matrix of patch features is decomposed to acquire the denoised features. Weighted fusion-based representation classifiers (WFRCs) with weighted multiplication are proposed to combine the sparse representation classifier (SRC) and the collaborative representation classifier (CRC) for floating raft detection, which can capture the competition between the floating raft and water surface as well as the collaboration within-class samples. Experiments on the study area of the Bohai Sea confirm that the proposed approach produces better results than some related methods. It is demonstrated that the WLRMF model extracts effective features and overcomes the influence of speckle noise at the same time, and the WFRC model is able to take advantages of the SRC in competition and CRC in collaboration for improving detection accuracies.