Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training.
Figure 2. Synthetic aperture radar images in three regions of the East China Sea.
Figure 3. Labeled samples used as the original labeled samples.
Figure 4. Three subimages and the detection results by the constant false alarm rate detector. The green boxes denote the detection results; the red boxes denote the ground truth.
Figure 5. Three subimages and the detection results by the Cascade R-CNN. The green boxes denote the detection results; the red boxes denote the ground truth.
Figure 6. Ship target discrimination results of subimage No. 3. a. Results by TT. b. Results by D-TT. c. Results by PS-TT. d. Results by the proposed method (correct detections are marked with red boxes, false alarms are marked with green boxes and are numbered, the missed ships are marked with blue boxes and are numbered).
Figure 7. Ship target discrimination results of subimage No. 4. a. Results by TT. b. Results by D-TT. c. Results by PS-TT. d. Results by the proposed method (correct detections are marked with red boxes, false alarms are marked with green boxes and are numbered, the missed ships are marked with blue boxes and are numbered).
Figure 8. Ship target discrimination results of subimage No. 5. a. Results by TT. b. Results by D-TT. c. Results by PS-TT. d. Results by the proposed method (correct detections are marked with red boxes, false alarms are marked with green boxes and are numbered, the missed ships are marked with blue boxes and are numbered).
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