SAR image ship target identification method based on multi-level feature depth fusion

The invention discloses an SAR (Synthetic Aperture Radar) image ship target identification method based on multi-level feature deep fusion. The SAR image ship target identification method comprises the following steps: (1) processing an SAR image sample by adopting a Haar-like feature template and reducing dimensions to obtain low-level Haar-like features; (2) processing the SAR image sample by using a convolutional neural network to obtain high-level depth features; (3) fusing the low-level Haar-like features and the high-level depth features by using a multi-level deep learning network to obtain a multi-level feature weight coefficient, and then obtaining an optimal SVM classifier through learning training; and (4) identifying the input SAR image sample slice to be identified by using the multi-level feature weight coefficient and the SVM classifier. According to the method, the detection performance of the SAR image ship target can be effectively improved, and the method has high engineering application va