Aircraft type recognition in satellite images

This paper proposes a hierarchical classification algorithm to accurately recognise aircrafts in satellite images. Since each aircraft in satellite images is captured far from the ground, it has a very small size and often includes various textures, orientations, dazzle paints, and even noise. All of these will present many challenges in extracting useful features and result in unstableness and inaccuracy of aircraft type recognition. Therefore, before recognition, a novel symmetry-based algorithm is proposed to estimate an aircraft's optimal orientation for rotation correction. In addition, several image preprocessing techniques such as noise removal, binarisation, and geometrical adjustments are also applied to removing the above variations. Then, distinguishable features are derived from each aircraft for aircraft recognition. However, different features have different discrimination abilities to recognise the types of aircrafts. 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 aircrafts 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 aircrafts in satellite images.