Recently, neural network has been increasingly applied to remote sensing imagery classification, The conventional neural network classifier performs learning from the representative information within a problem domain on a one-pixel-one-class basis; therefore, class mixture and the degree of membership of a pixel are generally not taken into account, often resulting in a poor classification accuracy, Based on the framework of a dynamic Learning neural network (DL), this communications proposes a fuzzy version (FDL) based on two steps: network representation of fuzzy logic and assignment of membership, Comparisons between the DL and FDL are made by applying both neural networks to SAR image classification, Experimental results show that the FDL has faster convergence rate than that of DL, In addition, the separability between similar classes is improved, Moreover, the classification results match better with ground truth.
關聯:
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
