Traditional binary change detection (BCD) in remote sensing focuses solely on detecting the occurrence of change, whereas semantic change detection (SCD) provides deeper insights by recognizing specific target categories before and after modifications, making it a more advanced approach for land cover SCD (LCSCD). However, current SCD approaches in remote sensing face challenges such as slight changes, irregular regions, and fuzzy boundaries in accurately detecting complex land cover changes, which limits their practical utility. Here, we propose an innovative SCD approach, DRGMS-Net, which utilizes a dual-resolution guided multiscale network for LCSCD. DRGMS-Net integrates semantic segmentation and BCD, which can efficiently extract essential features for accurately detecting changes. First, in the semantic information encoder, we utilize a dual-resolution network for extracting features at varying resolutions and propose a multiscale aggregation pyramid pooling module to extract multilayer features, providing more detailed and semantic features for LCSCD. Moreover, we introduce a strip attention module and feature interaction layer to extract features of irregular regions and fuse bitemporal features. In the semantic information decoder, we propose a change information aggregation module to better capture change features and enhance the representation of irregular objects. Finally, we design a combined loss based on PolyLoss and semantic loss to address the challenge caused by imbalanced pixels. We demonstrate the effectiveness of DRGMS-Net on two LCSCD datasets. The values of separation kappa are 20.84% and 44.21%, and the values of mean intersection over union are 72.22% and 84.39%. The results are superior to those of other state-of-the-art methods and highlight the potential of DRGMS-Net in providing detailed information about altered regions.