To address common challenges in change detection tasks, such as the loss of contextual feature information and insufficient utilization of data features that exploit only single-channel or spatial information, a high-resolution remote-sensing change detection algorithm based on context-aware Siam-UNet++ is proposed. This algorithm uses UNet++ as its backbone network and incorporates a transformer-style contextual transformer module to obtain contextual information from bitemporal images. Hence, more precise image change features are obtained. In addition, an ensemble attention module is employed to comprehensively utilize both channel and spatial information, which leads to higher precision and accuracy in change detection tasks. Experimental validation on the LEVIR-CD and WHU-CD datasets yields F₁ scores of 90.07% and 92.19% as well as intersection over union scores of 81.93% and 85.51%, respectively. Experimental results confirm the effective enhancement of the detection performance and accuracy of proposed algorithm.