基于多域多维特征融合的海面小目标检测

多维特征检测技术是提高海面小目标检测的有效途径。为了进一步提升海面小目标检测性能，本文提出基于多域多维特征融合的检测方法。首先，从时域、频域、时频域、极化域等多域，充分挖掘海杂波和含目标回波的差异性，并将这些差异性表征为多维特征，构建高维特征空间。其次，通过极化域和特征域的多维特征线性融合，将多维特征压缩到3D特征空间中，获得高维度信息的同时减少维度计算代价。然后，结合凸包学习算法获得3D判决区域，实现异常检测。最后，基于IPIX实测数据的实验结果表明:相对现有的极化检测器，提出的检测器具有25%以上的显著性能提升。 

Detection of Sea-surface Small Target Based on Multi-domain and Multi-dimensional Feature Fusion

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.