Addressing issues such as increased surface cover complexity, heightened heterogeneity within homogeneous regions, and greater similarity between different regions in high-resolution optical remote sensing images, which increase classification difficulty, a supervised learning method based on a dual neighborhood relationships Gaussian regression mixture model (GRMM), an improved version of the Gaussian mixture model (GMM), is proposed. First, supervised sampling of image regions is conducted, with histograms fitted using the least squares method to establish Gaussian mixture models for each land cover, rep-resenting the complex gray-scale features of land cover. Second, local spatial information of adjacent pixels is in-corporated into the image gray-scale space to construct a Gaussian regression model. Finally, in the membership space, neighborhood relationships are processed again to make classification decisions. GRMM achieves kappa coefficients of 97.2% on synthetic images and 98.5% on real high-resolution remote sensing images. Compared to existing mainstream models, GRMM demonstrates strong classification efficiency, noise reduction capability, and generalization ability, with clear classification boundaries, effectively enhancing the classification performance of high-resolution remote sensing images.