Change detection is an important technique providing insights to urban planning, resources monitoring, and environmental studies. For multispectral images, most semi-supervised change detection methods focus on improving the contribution of training samples hard to be classified to the trained classifier. However, hard training samples will weaken the discrimination of the training model for multispectral change detection. Besides, these methods only use the spectral information, while the limited spectral information cannot represent objects very well. In this paper, a method named as coarse-to-fine semi-supervised change detection is proposed to solve the aforementioned problems. First, a novel multiscale feature is exploited by concatenating the spectral vector of the pixel to be detected and its adjacent pixels by different scales. Second, the enhanced metric learning is proposed to acquire more discriminant metric by strengthening the contribution of training samples easy to be classified and weakening the contribution of training samples hard to be classified to the trained model. Finally, a coarse-to-fine strategy is adopted to detect testing samples from the viewpoint of distance metric and label information of neighborhood in spatial space. The coarse detection result obtained from the enhanced metric learning is used to guide the final detection. The effectiveness of our proposed method is verified on two real-life operating scenarios, Taizhou and Kunshan data sets. Extensive experimental results demonstrate that our proposed algorithm has better performance than those of other state-of-the-art algorithms.