Scene classification from remote sensing images using mid-level deep feature learning

Traditional remote sensing scene classification methods based on low-level local or global features easily lead to information loss, additionally, the influence of spatial correlation on scene images and the redundancy of feature representation are neglected. For overcoming these drawbacks, learnable multilayer energized locality constrained affine subspace coding (MELASC) - Convolutional Neural Network (CNN) framework (MELASC-CNN) which could generate orderless feature representation is proposed, and it considers both the diversity of local - global deep features and the redundancies of local geometric structure around visual words. Firstly, the energy of the basis is introduced to limit the number of neighbouring subspaces, moreover learnable locality-constrained affine subspace coding is presented for keeping the locality and sparsity of the corresponding coding vector, and otherwise, we utilize Gaussian Mixed Model (GMM) to improve the robustness of dictionary. Specifically, second-order coding based on information geometry is performed to further improve MELASC-CNN's performance; additionally, three kinds of proximity measures are proposed for describing closeness between features and affine subspaces. Finally, MELASC-CNN is built on the combination of the convolutional and fully connected layers for considering the global and local features. Simultaneously, MELASC-CNN extracts the feature vector at different resolutions through Spatial Pyramid Matching (SPM), and it integrates the spatial information into the final representation vector. For validation and comparison purposes, we conduct extensive experiments on two challenging high-resolution remote sensing datasets and show better performance than other related works.