Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed “from-to” change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively.

Semantic change detection (SCD) extends the traditional change detection (CD) task to simultaneously identify the change areas and their corresponding land cover categories in bi-temporal images. This “from-to” change information holds significant value in numerous practical applications and is increasingly garnering attention in the remote sensing domain. However, prevalent challenges such as loss of detail and class imbalance significantly hinder the efficacy of SCD applications. To address this challenge, we propose a novel semantic information collaboration network (SIC-Net). This network incorporates a detail capture path (DCP) and a spatial-temporal semantic coordination module (STSCM) aiming to effectively execute SCD by fusing detailed information with contextual features and harnessing synergies between binary CD and semantic segmentation. Additionally, we introduce a pseudo-label growth algorithm (PLGA) to mitigate the substantial loss of sample category information. The experimental results on two widely used SCD datasets demonstrate that SIC-Net outperforms other methods across various evaluation metrics, achieving SeK of 23.96% and 61.29%, respectively. These findings not only validate the effectiveness of the SIC-Net but also provide new ideas and directions for future research in the field of remote sensing, particularly in SCD. The source code is available at https://github.com/hy-pn/SICNet .