  Following the great success of curriculum learning in the area of machine
learning, a novel deep curriculum learning method proposed in this paper,
entitled DCL, particularly for the classification of fully polarimetric
synthetic aperture radar (PolSAR) data. This method utilizes the entropy-alpha
target decomposition method to estimate the degree of complexity of each PolSAR
image patch before applying it to the convolutional neural network (CNN). Also,
an accumulative mini-batch pacing function is used to introduce more difficult
patches to CNN.Experiments on the widely used data set of AIRSAR Flevoland
reveal that the proposed curriculum learning method can not only increase
classification accuracy but also lead to faster training convergence.
