The invention discloses a polarized SAR
image object classifying method based on multi-
quantum ridgelet representation. The polarized SAR
image object classifying method based on multi-
quantum ridgelet representation solves the problem of insufficient feature representation, low classification precision and high
time complexity of the prior art. The method is implemented through the steps of, firstly, extracting the image features of a polarized SAR image; secondly, combining the features into a
feature matrix and performing normalization; thirdly, selecting a training
data set and a testing
data set from the
feature matrix; fourthly, training the training
data set through a double-
quantum ridgelet network; fifthly, training and classifying the training data set through an
artificial neural network (NN) classifier; sixthly, classifying the
test data set through a trained classifier. By means of the multi-quantum ridgelet neural network, the polarized SAR
image object classifying method based on multi-quantum ridgelet representation is flexible in structure and improves the presentation ability of the image features of the polarized SAR image, thereby effectively improving the classification precision of the SAR image, reducing
time complexity and being applicable to classification of complex images.