As urbanization accelerates and the population continuously increases, the utilization and management of land resources have become increasingly important. The development of high-resolution remote sensing technology provides a new approach for detecting land cover changes. Currently, most remote sensing image change detection tasks mainly focus on detecting significant changes in buildings, and there is a lack of research on detecting changes in land cover categories. In this study, based on a public dataset, more land cover change scenarios are annotated. Combining the original semantic segmentation backbone network with a Siamese network structure, this study proposes a detection model suitable for tasks of detecting changes in land cover categories. The model incorporates a change guidance module in the feature extraction stage to assist the network in focusing on change information in the two temporal images. A channel information interaction module is added at different stages of the network to enhance the fusion of information from different feature maps. Additionally, a feature alignment module is added to the last layer of the feature extraction stage to alleviate feature offset caused by downsampling. Experimental results on a dataset of detecting changes in land cover categories demonstrate that the proposed method can effectively extract change information from the image and improve the segmentation accuracy.