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.

本文介绍了一种基于无监督浅层卷积神经网络（USCNN）融合方法进行变化检测，通过使用不同大小的卷积核对双时相图像进行多尺度信息提取，将相同尺度的差异特征图通过 1*1 卷积层融合为一个特征图，最后通过 1*1 卷积层融合不同尺度的输出特征，实现图像的多尺度信息融合。实验证明了该方法在四个真实遥感数据集上的可行性和有效性。