In recent years, constrained by the challenges associated with expensive data annotation and poor generalization ability in supervised models, domain adaptation has been proposed to effectively mitigate domain gaps, which has gained significant attention in the field of remote sensing. However, most existing methods do not consider the inherent characteristics of remote sensing images when formulating adaptive strategies. In addition, there has been limited research on addressing class-imbalanced data situations, leading to undesirable performance in domain adaptation tasks involving long-tailed datasets. To overcome the aforementioned limitations, based on the analysis of intraclass diversity and intradomain style differences of remote sensing images, we propose a novel prototype contrastive learning framework called general-specific prototype contrastive learning (GSPCL). Due to the unreliability of clustering samples in conventional prototype-based clustering methods, the bidirectional weighted prototype strategy is proposed to optimize this loophole. Consequently, more robust prototypes are constructed in both domains, serving as mediators to reduce domain discrepancies bidirectionally. Particularly, often overlooked in most methods, we incorporate low-confidence sample features into the contrastive learning process alongside these prototypes to further guide the model to address feature alignment and long-tail issues effectively. Finally, in order to verify the superiority of our proposed method, we adhere to two existing experimental settings and construct an extra optical remote sensing domain adaptation dataset with class-imbalanced scenarios. In the first two experimental settings, GSPCL outperforms the second-ranked approach by 5.0% and 4.0% in average accuracy. Furthermore, our approach exhibits highly competitive results in handling long-tailed data scenarios.