An Augmentation Attention Mechanism for High-Spatial-Resolution Remote Sensing Image Scene Classification

High-spatial-resolution remote sensing (HRRS) image scene classification, which categorizes HRRS images into an independent set of semantic-level land use and land cover classes based on image contents, has attracted much attention, and many methods have been proposed due to its wide application in earth observation tasks. In fact, categories of HRRS images depend on regions containing class-specific ground objects, while most of the existing methods for HRRS image scene classification only focus on global information, which introduces redundant information and results in the poor performance of HRRS image scene classification. To overcome the shortcomings of the existing methods, an attention mechanism-based convolutional neural network with multiaugmented schemes is proposed in this article. In the proposed method, augmentation operations over attention mechanism feature maps are used to force the model to capture class-specific features and eliminate redundant information and push the model to capture discriminative regions as much as possible, instead of using all global information without favor. Moreover, a bilinear pooling is utilized to expand the interclass discrimination. Still, feature center loss motivated by center loss is applied to narrow the intraclass gap. To verify the effectiveness of the proposed end-to-end model, three benchmarks are used for testing, and the experimental results have proven the superiority of the proposed method, compared with current state-of-the-art end-to-end methods for HRRS image scene classification.