To solve the problem of inaccurate segmentation of edge targets and poor classification results in the traditional DeepLabv3+ algorithm in remote sensing image change detection, an improved DeepLabv3+ high-resolution remote sensing image change detection method is proposed. First, a DeepLabv3+ model is developed based on deep separation and hole convolutions, which significantly reduces the amount of calculation and model parameters. Second, the pooling pyramid structure is improved by introducing different receptive fields. Moreover, multiscale feature tensors are added to the decoder module; the intermediate stream structure is reconstructed; and the Xception backbone network is optimized. Then, the network channel is adjusted by setting weight coefficients. The weight configuration is optimized to improve the DeepLabv3+ model. Finally, non-generative and generative sample expansion methods are used to develop the dataset. The detection accuracy and generalization performance of the proposed method are confirmed via experimental comparison and analysis. The experimental results demonstrate that the proposed method can effectively improve the output resolution and detailed characteristics of graphics. This shows that the proposed method has good generalization performance and higher detection accuracy compared to other traditional methods. Furthermore, the proposed method has the highest image detection accuracy compared with other traditional methods, and the overall accuracy index can reach 96.4%.