Change detection is a significant issue for understanding the changes occurring on the land surface. We propose a change detection approach based on a semantic segmentation network from multispectral (MS) images. Different from the traditional approaches that learn deep features from the change index or establish mapping relations from patches, the proposed approach employs the semantic segmentation network UNet++ for end-to-end change detection. Nevertheless, in UNet++, the deep feature is directly upsampled from the node in the lower level and does not involve much information from the nodes in the other levels. To cope with this problem and further enhance its robustness, the zigzag UNet++ (ZUNet++) is developed. In ZUNet++, the zigzag connection between nodes can be found, so the inputs of the node involve not only the upsampled deep feature but also the downsampled shallow feature, i.e., the network fuses multiple feature information. In addition, as few MS training datasets are available, we designed a strategy in which each MS image is transferred into several pseudo-RGB images; thus the network is trained by available RGB training sets and can be applied to the testing MS datasets. In the experiment, three real testing MS datasets that reflect different types of changes in Xi’an City are used. Experimental results show that, upon determining the appropriate parameter, the proposed ZUNet++ outperforms the other state-of-the-art approaches, demonstrating its feasibility and effectiveness.