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.

中文翻译：

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一种用于高空间分辨率遥感图像场景分类的增强注意机制

高空间分辨率遥感 (HRRS) 图像场景分类，根据图像内容将 HRRS 图像分类为一组独立的语义级土地利用和土地覆盖类别，引起了广泛关注，并提出了许多方法。它在地球观测任务中的广泛应用。事实上，HRRS图像的类别依赖于包含特定类别地物的区域，而现有的HRRS图像场景分类方法大多只关注全局信息，引入了冗余信息，导致HRRS图像场景分类性能不佳。为了克服现有方法的缺点，本文提出了一种基于注意力机制的具有多重增强方案的卷积神经网络。在提出的方法中，对注意力机制特征图的增强操作用于强制模型捕获特定于类的特征并消除冗余信息，并推动模型尽可能多地捕获判别区域，而不是使用所有全局信息而不是使用所有全局信息。此外，利用双线性池化来扩大类间区分。尽管如此，由中心损失驱动的特征中心损失被应用于缩小类内差距。为了验证所提出的端到端模型的有效性，使用三个基准进行测试，与当前最先进的端到端方法相比，实验结果证明了所提出的方法的优越性用于 HRRS 图像场景分类。