The precise segmentation of greenhouses using remote sensing images is crucial for the advancement of large-scale precision agriculture. However, existing segmentation methods often face challenges such as overfitting caused by redundant features and insufficient robustness to seasonal variations in spectral features. To address these issues, we propose the RLUNet model, which combines the ReliefF algorithm with a U-Net structure to optimize feature selection. The proposed model accurately identifies greenhouses by capturing multiscale seasonal variation features and integrating shallow spectral information with deep semantic data. The experimental results demonstrate that the proposed RLUNet model outperforms the baseline model in terms of segmentation accuracy, edge contour clarity, and gap pixel recognition. Specifically, overall accuracy improves by 0.06 to 1.11 percentage points, the intersection-over-union ratio increases by 5.63 to 13.41 percentage points, and the F1 score increases by 3.05 to 10.23 percentage points, effectively addressing feature redundancy issues. Furthermore, cross-validation with different images confirms the model’s robustness against seasonal spectral variations. This approach offers a reliable solution for greenhouse segmentation and holds substantial potential for applications in precision agriculture.