  With the rapid development of deep learning, a variety of change detection
methods based on deep learning have emerged in recent years. However, these
methods usually require a large number of training samples to train the network
model, so it is very expensive. In this paper, we introduce a completely
unsupervised shallow convolutional neural network (USCNN) fusion approach for
change detection. Firstly, the bi-temporal images are transformed into
different feature spaces by using convolution kernels of different sizes to
extract multi-scale information of the images. Secondly, the output features of
bi-temporal images at the same convolution kernels are subtracted to obtain the
corresponding difference images, and the difference feature images at the same
scale are fused into one feature image by using 1 * 1 convolution layer.
Finally, the output features of different scales are concatenated and a 1 * 1
convolution layer is used to fuse the multi-scale information of the image. The
model parameters are obtained by a redesigned sparse function. Our model has
three features: the entire training process is conducted in an unsupervised
manner, the network architecture is shallow, and the objective function is
sparse. Thus, it can be seen as a kind of lightweight network model.
Experimental results on four real remote sensing datasets indicate the
feasibility and effectiveness of the proposed approach.
