Remote sensing image dehazing is a key technology to improve the quality and visibility of images acquired from aerial or satellite platforms. It is widely used in many fields such as environmental monitoring, urban planning, disaster management and military reconnaissance. However, existing single-stage and multi-stage dehazing methods have limitations in processing complex scenes, detail recovery and haze removal. To this end, this paper proposes a novel Siamese Dual-stage Adaptive Dehazing Network (SDAD-Net). By introducing a Siamese sub-network with partially shared weights, the network can learn dehazing knowledge from each other at different stages, thereby enhancing the ability to constrain haze areas. The two sub-networks perform different degrees of dehazing on the image in two-stages. The output of the first-stage provides a prior for the second-stage and improves the image reconstruction effect. The network uses a Hierarchical Residual Fusion Module (HRFM) to perform multi-scale information fusion to provide richer information for image dehazing. Experimental results show that SDAD-Net performs better than existing dehazing methods on public remote sensing image datasets, especially in detail preservation and complex scene processing.