Currently, satellite remote sensing images feature a large size, and the targets to be detected are mostly small and unevenly distributed, with many targets gathering together. There are also significant scale differences among targets and the background is rather complex. All these factors pose great challenges to land utilization and environmental disaster detection. Therefore, this study proposes a satellite remote sensing image object detection method based on improved YOLO11. Firstly, this study introduces an attention mechanism into the C3k2 module of YOLO11 and designs the C3k2_DAB module. This enhances the detection performance under the influence of complex backgrounds while controlling model complexity. Secondly, a PKI module is added to the neck network to boost adaptive feature extraction of local and global context information. Finally, a new detection head PConv is introduced at the detection end to extract spatial features more swiftly under the prerequisite of reducing redundant computations and memory access. Experimental results demonstrate that the improved YOLO11 network model yields excellent performance in remote sensing image target detection. Compared to the original YOLO11 model, the proposed model’s mAP@0.5 increases by 2.4% and mAP@0.5:0.95 improves by 2.1%. Additionally, this model outperforms other mainstream target detection models, thus providing new insights for the application of remote sensing target detection algorithms.