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Small sample PolSAR image classification method based on fuzzy label semantic prior

ActiveCN110096994AEnsure consistencyAvoid the problem of increased calculationScene recognitionNeural architecturesSmall sampleAlgorithm
The invention discloses a small sample PolSAR image classification method based on fuzzy label semantic prior. The method comprises the steps of preparing a PolSAR image to be classified; obtaining real polarization characteristics as input data of the network; obtaining a sampling matrix for recording the position of the training sample and a sampling label matrix for recording pixel label information at the corresponding position; utilizing the sampling label matrix to initialize and classify to build a full convolutional network FCN; sending the real-number input data, the sampling matrix,the sampling label matrix and the classification matrix to a built full convolutional network FCN for training; updating the classification matrix by utilizing the prediction result of the FCN, the sampling matrix, the sampling label matrix and the current state of the classification matrix; repeating the operation until the maximum number of iterations is met; outputting the final classificationmatrix; and calculating classification accuracy and a classification result graph to complete image classification. According to the invention, alternate iteration training is carried out on the deepfull convolution network parameters and the label category variables, and the problem of low PolSAR classification precision under a small sample problem is solved.
Owner:XIDIAN UNIV
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