The object detection of remote sensing imagery has lower detection accuracy caused by complexity of background, target appearance variety and arbitrary orientation. In order to solve the problem, a method based on strongly-supervised deformable part models was proposed. Then multiple sub-models in each direction range of the object were trained. In addition, the object bounding rectangle, position and semantic information of every part were labeled. In the model training stage, firstly, multi-scale Histogram of Oriented Gradients (HOG) feature pyramid for every training image was constructed, and the model structure was initialized according to object-part label information and Minimum Span Tree (MST). Secondly, the sub-models corresponding to every direction region were trained using Latent Support Vector Machine (LSVM). Every sub-model was consisted of a object filter, multiple twice resolution part filters, and the position relation model. Finally, the mixture model was merged from all sub-models to detect object. In the object detection stage, the multi-scale feature pyramid was also firstly constructed, then matching response score in feature pyramid was computed using mixture training filter model and sliding window. Optimized detection results could be obtained by setting threshold for the response score and adopting Non-Maximum Suppression (NMS) algorithm. The object detection accuracy of the proposed method is 89.4% on self-built remote sensing data sets, compared to the highest accuracy among weakly-supervised Deformable Part Model (DPM), Exemplar Support Vector Machines (Exemplar-SVMs) and Histogram of Oriented Gradients-Support Vector Machine (HOG-SVM), the proposed algorithm has an improvement about 4 percentage points in detection behavior. The experimental results show that the proposed algorithm could has some improvement in solving above mentioned direction and background complex problems, and also can be applied in object detection of the airport military airplane.