We present a method for detecting inshore ships using high-resolution synthetic aperture radar (SAR) images. The method first applies preprocessing steps to obtain the solving region of an SAR image, suitable for the berthing rules. Then the ship candidates are extracted based on a maximally stable extremal region detector, while ship detection is applied using an improved constant false alarm rate (CFAR) detector. Finally, based on discrimination processing, false alarms are removed according to compactness, therefore the ultimate detection result is obtained without interference from strong terrestrial clutter or man-made objects. Experimental results show that this method could achieve effective detection of ships in harbors using high-resolution SAR images. This method can reduce the required computation time by 99.7%, compared with the traditional CFAR method.
Vessel classification using inverse synthetic aperture radar (ISAR) imagery is important because it can be used for maritime surveillance and has a high military value. We propose a vessel classification algorithm based on multifeature joint matching. We first utilize a preprocessing method to eliminate the vessel wakes and strong sea clutter, which interfere with feature extraction. In view of the different categories of vessels, we then propose a new two-dimensional strong scattering points encoding (SSPE2-D) for vessel recognition. Furthermore, we modify the method to calculate the number of peaks in the range profile in order to obtain a more accurate result. The high-resolution ISAR images obtained as a result are used to verify the effectiveness of our method. We also compare our proposed method with three other classification methods, and show that the classification rate obtained using our technique is more accurate than that from each of the other methods. Our experiments also show that the preprocessing and the new encoding feature improve classification accuracy.
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