Sea surface target detection is a key stage in a typical target detection system and directly influences the performance of the whole system. As an effective discriminator, the one-class support vector machine (OCSVM) has been widely used in target detection. In OCSCM, training samples are first mapped to the hypersphere in the kernel space with the Gaussian kernel function, and then, a linear classification hyperplane is constructed in each cluster to separate target samples from other classes of samples. However, when the distribution of the original data is complex, the transformed data in the kernel space may be nonlinearly separable. In this situation, OCSVM cannot classify the data correctly, because only a linear hyperplane is constructed in the kernel space. To solve this problem, a novel one-class classification algorithm, referred to as ensemble one-class support vector machine (En-OCSVM), is proposed in this paper. En-OCSVM is a hybrid model based on $k$-means clustering and OCSVM. In En-OCSVM, training samples are clustered in the kernel space with the $k$-means clustering algorithm, while a linear decision hyperplane is constructed in each cluster. With the combination of multiple linear classification hyperplanes, a complex nonlinear classification boundary can be achieved in the kernel space. Moreover, the joint optimization of the $k$-means clustering model and OCSVM model is realized in the proposed method, which ensures the linear separability of each cluster. The experimental results based on the synthetic dataset, benchmark datasets, IPIX datasets, and SAR real data demonstrate the better performance of our method over other related methods.

The complex and dynamic nature of the marine environment poses significant challenges for sea surface target detection. Traditional methods relying on single-domain features suffer from performance degradation under varying conditions. To address this limitation, a multi-domain polarization-aware feature fusion network capable of controlling the false alarm rate (MP-FFN) for robust sea surface target detection is proposed in this paper. The proposed method first extracts discriminative radar echo features from time, frequency, fractal, and polarization domains. Subsequently, autoencoder-based intra-domain network is employed to reduce feature dimensionality while minimizing information loss. These compressed features are then fused through a multi-layer perceptron (MLP)-based inter-domain network, enabling comprehensive cross-domain correlation learning. Moreover, a controllable false alarm rate is achieved through a customized loss function. Extensive experiments on the IPIX radar dataset demonstrate that the proposed method outperforms traditional feature-based detection methods, exhibiting superior robustness and detection accuracy in diverse marine environments.