Local structure learning in high resolution remote sensing image retrieval

High resolution remote sensing image captured by the satellites or the aircraft is of great help for military and civilian applications. In recent years, with an increasing amount of high resolution remote sensing images, it becomes more and more urgent to find a way to retrieve them. In this case, a few methods based on the statistical information of the local features are proposed, which have achieved good performances. However, most of the methods do not take the topological structure of the features into account. In this paper, we propose a new method to represent these images, by taking the structural information into consideration. The main contributions of this paper include: (1) mapping the features into a manifold space by a Lipschitz smooth function to enhance the representation ability of the features; (2) training an anchor set with several regularization constrains to get the intrinsic manifold structure. In the experiments, the method is applied to two challenging remote sensing image datasets: UC Merced land use dataset and Sydney dataset. Compared to the state-of-the-art approaches, the proposed method can achieve a more robust and commendable performance.