Projection learning with local and global consistency constraints for scene classification

Besides the problem of high within-class variation and between-class ambiguity in high spatial resolution (HSR) remote sensing images, the dimension of data representation is very high, which poses a challenge for scene classification. To achieve high scene classification performance, it is important to uncover a discriminative subspace for data representation and scene classification. In this paper, we propose a projection learning framework with local and global consistency constraints for aerial scene classification. During the learning process, the within-class compactness and between-class separation of the data representation are enforced. To guarantee the subspace to be locally smooth, we obtain the local geometric structures of data including the similarity of features and geospatial adjacency of image patches. We utilize the global label consistency constraint to enforce the discrimination of the subspace. To make the projection optimal for classification, the projection learning with local and global constraints is integrated with the classification error to form a unified objective function. An efficient iteration algorithm is employed to solve the objective function. Experimental results demonstrate the superior performance of our method over state-of-the-art algorithms on aerial scene classification tasks.