Abstract: Multi-dimensional feature detection technology is an effective way to improve detection performance of sea-surface small targets. A detection method based on multi-domain and multi-dimensional feature fusion is proposed to further improve performance in this paper. First, the differences between sea clutter and target returns are fully explored in time domain, frequency domain, time-frequency domain and polarization domain, which are represented as multi-dimensional features to construct high-dimensional feature space. Second, multi-dimensional features are compressed into 3-dimentional feature space by the linear fusion in polarization domain and feature domain, which can obtain high-dimensional information and reduce dimensional computational cost at the same time. Third, convex hull learning algorithm is used to obtain the 3D decision region and realize the anomaly detection. Finally, experimental results via IPIX data show that the proposed detector can attain significant performance improvement of more than 25%, relative to the existing polarization detectors.