The deployment of landslide intelligent recognition models in non-training regions encounters substantial challenges, primarily attributed to heterogeneous remote sensing acquisition parameters and inherent geospatial variability in factors such as topography, vegetation cover, and soil characteristics across distinct geographic zones. Addressing the issue of underutilization of landslide contextual information and morphological integrity in domain adaptation methods, this paper introduces a cross-domain landslide extraction approach that integrates image masking with enhanced morphological information. Specifically, our approach implements a pixel-level mask on target domain imagery, facilitating the utilization of context information from the masked images. Furthermore, it establishes a morphological information extraction module, grounded in predefined thresholds and rules, to produce morphological pseudo-labels for the target domain. The results demonstrate that our method achieves an IoU (intersection over union) improvement of 1.78% and 6.02% over the suboptimal method in two cross-domain tasks, respectively, and a remarkable performance enhancement of 33.13% and 31.79% compared to scenarios without domain adaptation. This cross-domain extraction method not only substantially boosts the accuracy of cross-domain landslide identification but also enhances the completeness of landslide morphology information, offering robust technical support for landslide disaster monitoring and early warning systems.