Compact Global-Local Convolutional Network With Multifeature Fusion and Learning for Scene Classification in Synthetic Aperture Radar Imagery

Abstract

Feature learning of convolutional neural networks (CNNs) has gained considerable attention and achieved good performance on synthetic aperture radar (SAR) image scene classification. However, the performance of the existing convolutional feature learning methods is limited for generating the distinguishable feature representations because such techniques inherently suffer from shortcomings, i.e., they do not consider the local feature distribution of deep orderless feature statistics and deep orderless multifeature learning style. To alleviate these drawbacks, we propose a compact global-local convolutional network with multifeature fusion and learning (CGML) for SAR image scene classification, which contains double branches of convolutional feature learning net (C-net) and local feature distribution learning net (L-net). L-net employs the localized and parameterized affine subspace coding layer for local feature distribution learning and captures the feature statistics of each cluster center via detailed local feature division. The standard convolutional feature map is utilized for the convolutional feature learning in C-net. Subsequently, the compact multifeature fusion and learning strategy captures the compact global second-order orderless feature representation and allows the double branches to interact with each other via the tensor sketch algorithm. Especially, the feature learning strategy of L-net is defined in affine subspace which fully characterizes the feature distribution inside each cluster space. Finally, we concatenate the outputs of the multifeature fusion and learning network, then pool and feed them into softmax loss. Based on extensive evaluations on TerraSAR-X1 and TerraSAR-X2 image scene classification datasets, CGML can yield superior performances when compared with those of several state-of-the-art networks.