  This work develops a novel end-to-end deep unsupervised learning method based
on convolutional neural network (CNN) with pseudo-classes for remote sensing
scene representation. First, we introduce center points as the centers of the
pseudo classes and the training samples can be allocated with pseudo labels
based on the center points. Therefore, the CNN model, which is used to extract
features from the scenes, can be trained supervised with the pseudo labels.
Moreover, a pseudo-center loss is developed to decrease the variance between
the samples and the corresponding pseudo center point. The pseudo-center loss
is important since it can update both the center points with the training
samples and the CNN model with the center points in the training process
simultaneously. Finally, joint learning of the pseudo-center loss and the
pseudo softmax loss which is formulated with the samples and the pseudo labels
is developed for unsupervised remote sensing scene representation to obtain
discriminative representations from the scenes. Experiments are conducted over
two commonly used remote sensing scene datasets to validate the effectiveness
of the proposed method and the experimental results show the superiority of the
proposed method when compared with other state-of-the-art methods.
