Currently, remote sensing image change detection methods based on deep learning are still not effective enough to be satisfactory when dealing with images with significant scale changes, and most of the methods lack effective interactions between different layers of features in the decoding stage. Aiming at the above problems, the paper proposes a high-resolution remote sensing image change detection method based on scale-aware and spatial selection hierarchical interaction in view of the classical U-net network. Firstly, a scale-aware module is designed by introducing channel attention after extracting features through chunked parallel depthwise separable convolutions of different sizes, in order to efficiently extract changing objects with different shape scales. Then, by utilizing spatial attention cross-enhancement between shallow and deep features, a spatial selection hierarchical interaction module is presented to refine the representational capabilities of the features. Finally, based on the difference maps of the two remote sensing images, a difference multi-scale attention module is given to highlight the changed information and suppress the unchanged information. The method proposed in the paper achieves F₁ scores (the harmonic mean of precision and recall) of 91.72%, 85.17%, 90.82%, and 88.03% on four public datasets: WHU, Google, LEVIR, and GVLM, respectively. Compared to existing six change detection networks such as FC-EF, FC-Conc, IFN, SNUNet, BIT, and MSCANet, the F₁ score is significantly improved.