SHIP CLASSIFICATION OF SAR IMAGE BASED ON CONVOLUTIONAL NEURAL NETWORKS
In view of the problem that speckled noise in synthetic aperture radar(SAR)image leads to low accuracy of image classification,a classification algorithm based on improved VGG16 is proposed.A layer of attention was added to the convolution layer to focus on important features and suppress unimportant features,so as to suppress speckled noise.The Fisher loss function was introduced in the objective function,which was used to restrain the within-class and between-class distance of the feature,so as to reduce the classification errors caused by speckle noise.Through the experiments,it can be seen that the classification accuracy is improved by 5.63 percentage points,compared with the original network,which can effectively improve the problem of low classification accuracy caused by speckled noise.
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