目标检测作为航天遥感领域的重要应用方向，在灾害应急、资源管理、环境监测等领域具有重要意义。以船只目标为例，研究航天遥感图像目标检测。航天遥感观测范围覆盖全球不同海域，这些海域的地貌特征存在巨大差异，现有舰船目标检测方法多采用统一结构处理不同场景，难以适应不同区域及不同分辨率下地物特征的变化。将混合专家模型（MoE）引入航天遥感图像目标检测任务，提出了一种基于混合专家模型的航天遥感图像目标检测模型。该模型针对不同地理区域的差异特征，自适应地选择相应的专家处理，实现不同海域下更精准的舰船目标检测。模型包含两个专家组：一组由空间分辨率信息引导，以捕获不同分辨率下的地物尺度差异；另一组由经纬度信息引导，以适应不同地理区域的地物分布特征。通过两组专家的协同工作，模型能够更加精准地感知和定位图像中的船只目标，显著提高检测性能。此外，还构建了一个包含多分辨率信息和经纬度信息的数据集——地理信息舰船检测数据集（GISD），为研究提供数据支撑。在GISD数据集和公开数据集LEVIR-ship上进行了实验验证，本模型的平均精度AP50分别达到了80.6%、85.7%，相较于两个数据集上性能最优模型的AP50提升了3.8%、2.0%。

As an important application direction in the field of spaceborne remote sensing， object detection is of great significance in disaster emergency response， resource management， environmental monitoring and other fields. We take ship targets as an example to study object detection in spaceborne remote sensing images. Spaceborne remote sensing covers sea areas in different regions worldwide， and these sea areas have significant differences in geomorphic features. Existing ship target detection methods mostly use a unified structure to handle different scenarios， making it difficult to adapt to changes in ground object features in different regions and with different resolutions. To address this， we introduce the Mixture of Experts （MoE） model into the task of spaceborne remote sensing image object detection， and propose a spaceborne remote sensing image object detection model based on the Mixture of Experts model. It adaptively selects corresponding expert processing for the differential features of different geographical regions to achieve more accurate ship target detection in different sea areas. The model includes two groups of experts： one group is guided by spatial resolution information to capture differences in ground object scales under different resolutions； the other group uses latitude and longitude information for guidance to adapt to the distribution characteristics of ground objects in different geographical regions. Through the collaborative work of the two groups of experts， the model can more accurately perceive and locate ship targets in images， significantly improving detection performance. In addition， we construct a dataset containing multi-resolution information and latitude and longitude information-the Geographic In-formation Ship Detection Dataset （GISD） to provide data support for the research. We conduct experimental verification on the GISD dataset and the public dataset LEVIR-ship. The average precision AP50 of the proposed method reaches 80.6% and 85.7% respectively， which is 3.8% and 2.0% higher than that of the best-performing models on the two datasets.