Ship ISAR Image Classification with Probabilistic Neural Network

نویسنده

  • B. Mamatha
چکیده

Ship detection and classification plays a significant role in naval warfare. Inverse Synthetic Aperture Radar (ISAR) images are being used extensively for feature extraction in ship detection and classification. The classification problem is solved in two steps. The first step is the extraction of features that characterize the target. The second step is to feed the computed feature values to a classifier to assign the target to one of the known classes stored in the database. In this paper, digital image processing techniques are used to extract target features from ship ISAR images for identification and classification. The feature vectors are computed from three different techniques. They are statistical moments, Zernike moments and polar transforms. These feature vectors obtained from the techniques mentioned are given as input to neural network based classifier. Here the Probabilistic neural network is implemented to classify the ship ISAR images. From the results obtained, the classification accuracy was found to be satisfactory with all the feature vectors.

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تاریخ انتشار 2017