The accurate detection of small, densely packed and arbitrarily oriented aircraft in high-resolution remote sensing imagery remains highly challenging due to significant variations in object scale, orientation and background complexity. Existing detection frameworks often struggle with insufficient representation of small objects, instability of rotated bounding box regression and inability to adapt to complex background. To address these limitations, we propose SPOD-YOLO, a novel detection framework specifically designed for small aircraft in remote sensing images. This method is based on YOLOv11, combined with the feature attention mechanism of swintransformer, through targeted improvements on cross-scale feature modelling, dynamic convolutional adaptation, and rotational geometry optimization to achieve effective detection. Additionally, we have constructed a new dataset based on satellite remote sensing images, which has high density of small aircraft with rotated bounding box annotations to provide more realistic and challenging evaluation settings. Extensive experiments on MAR20, UCAS-AOD and the constructed dataset demonstrate that our method achieves consistent performance gains over state-of-the-art approaches. SPOD-YOLO achieves an 4.54% increase in mAP50 and a 11.78% gain in mAP50:95 with only 3.77 million parameters on the constructed dataset. These results validate the effectiveness and robustness of our approach in complex remote sensing scenarios, offering a practical advancement for the detection of small objects in aerospace imagery.