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Aeroplane detection in very-high-resolution images using deep feature representation and rotation-invariant Hough forests

ABSTRACT This letter proposes a processing chain for detecting aeroplanes from very high-resolution (VHR) remotely sensed images with the fusion of deep feature representation and rotation-invariant Hough forests. First, superpixel segmentation is used to generate meaningful and non-redundant patches. Second, deep learning techniques are exploited to construct a multi-layer feature encoder for representing high-order features of patches. Third, a set of multi-scale rotation-invariant Hough forests are trained to detect aeroplanes of varying orientations and sizes. Experiments show that the proposed method is a promising solution for detecting aeroplanes from VHR remotely sensed images, with a completeness, correctness, and F-measure of 0.956, 0.970, and 0.963, respectively. Comparative studies with four existing methods also demonstrate that the proposed method outperforms the other existing methods in accurately detecting aeroplanes of varying appearances, orientations, and sizes.