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This paper proposes a hierarchical classification algorithm to accurately recognise aircraft in satellite images. Before recognition, a novel symmetry-based algorithm is proposed to estimate an aircraft's optimal orientation for rotation correction. Then, distinguishable features are derived from each aircraft for aircraft recognition. However, different features have different discrimination abilities to recognise the types of aircraft. Therefore, a novel booting algorithm is proposed to learn a set of proper weights from training samples for feature integration. Owing to this integration, significant improvements in terms of recognition accuracy and robustness can be achieved. Last, a hierarchical recognition scheme is proposed to recognise the types of aircraft by using the area feature, first for a rough categorisation on which detailed classifications are then achieved using several suggested features. Experiments were conducted on a wide variety of satellite images. Experimental results reveal the feasibility and validity of the proposed approach in recognising aircraft in satellite images.