Single-sample aeroplane detection in high-resolution optimal remote sensing imagery

In remote sensing images, detecting aeroplanes of special shapes is difficult due to limited number of samples. Without enough training samples, most supervised learning based algorithms will fail. Focusing on the specially-shaped aeroplanes in high-resolution optical remote sensing imagery, this paper presents a single-sample approach. The proposed approach takes one sample as input and directly searches for similar matches from the image. Unlike the supervised learning algorithms which extracts information from positive and negative samples, the hyperspectral algorithm estimates the statistics of background by analyzing the global information of the target image, needless to provide negative samples. Furthermore, this algorithm tries to find a hyperplane projected on which the background is compressed while the target is preserved, making it more data-adaptive than the conventional similarity measurements. Experiments on real data have presented the robustness of the proposed method.