High-Order Generalized Orderless Pooling Networks for Synthetic-Aperture Radar Scene Classification

Abstract

Fixed coding style in bag of visual words (BOVW) model and strong spatial information in convolutional neural network (CNN) feature representation make the feature vector less adaptable for scene classification. With the purpose of extracting the learnable orderless feature for SAR scene classification, the high-order generalized orderless pooling network trained by backpropagation is proposed for learning the high-order vector of locally aggregated descriptors (VLADs) and locality constrained affine subspace coding (LASC), compared with the first-order feature coding style, the proposed network could learn high-order coding features by outer product automatically. Subsequently, for making the feature representation more powerful, the matrix normalization (square root) whose gradients are computed via singular value decomposition (SVD) and elementwise normalization are introduced into the proposed network. Finally, experiments on the SAR scene classification data set from TerraSAR-X image show the proposed networks achieve better performance than the state-of-the-art approaches.