Deep Depthwise Separable Convolutional Network for Change Detection in Optical Aerial Images
Ruochen Liu, D. Jiang, Langlang Zhang, Zetong Zhang
Abstract
In this article, a remote sensing image change detection method based on depthwise separable convolution with U-Net is proposed, which omits the tedious steps of generating and analyzing the difference map in the traditional remote sensing image change detection method. First, two images having c-channel each can be specifically stacked into a 2c-channel image, and the change detection can be converted to an image segmentation problem, an improved full convolution network (FCN) called U-Net is exploited to directly separate the changing regions. Because the capability of the deep convolution network is proportional to the depth of the network and a deeper convolution network means the increase of the training parameters, we then replace the original convolution in FCN by the depthwise separable convolution, making the entire network lighter, while the model performs slightly better than the traditional convolution operation. Besides that, another innovation in our proposed method is to use a preference control loss function to meet the different needs of precision and recall rate. Experimental results validate the effectiveness and robustness of the proposed method.