  Optical aerial images change detection is an important task in earth
observation and has been extensively investigated in the past few decades.
Generally, the supervised change detection methods with superior performance
require a large amount of labeled training data which is obtained by manual
annotation with high cost. In this paper, we present a novel unsupervised
self-training algorithm (USTA) for optical aerial images change detection. The
traditional method such as change vector analysis is used to generate the
pseudo labels. We use these pseudo labels to train a well designed
convolutional neural network. The network is used as a teacher to classify the
original multitemporal images to generate another set of pseudo labels. Then
two set of pseudo labels are used to jointly train a student network with the
same structure as the teacher. The final change detection result can be
obtained by the trained student network. Besides, we design an image filter to
control the usage of change information in the pseudo labels in the training
process of the network. The whole process of the algorithm is an unsupervised
process without manually marked labels. Experimental results on the real
datasets demonstrate competitive performance of our proposed method.
