Scene Classification With Recurrent Attention of VHR Remote Sensing Images
Scene Classification With Recurrent Attention of VHR Remote Sensing Images
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DOI：
10.1109/tgrs.2018.2864987
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发表时间：
2019-02
影响因子：
8.2
通讯作者：
Qi Wang;Shaoteng Liu;J. Chanussot;Xuelong Li
中科院分区：
文献类型：
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作者：
Qi Wang;Shaoteng Liu;J. Chanussot;Xuelong Li
遥感图像场景分类因其广泛的应用而受到广泛关注。在本文中，与人类视觉系统（HVS）的指导下，我们探索的注意力机制，并提出了一种新的端到端的注意力递归卷积网络（ARCNet）的场景分类。它可以学习有选择地集中在一些关键区域或位置，并只处理它们的高级别特征，从而丢弃非关键信息，提高分类性能。本文的贡献有三个方面。首先，我们设计了一种新的循环注意力结构，将高层语义和空间特征压缩到几个单纯形向量中，以减少学习参数。其次，提出了一个名为ARCNet的端到端网络，自适应地选择一系列的注意区域，然后通过学习顺序处理它们来生成强大的预测。第三，我们构建了一个名为OPTIMAL-31的新数据集，它包含了比流行数据集更多的类别，为研究人员提供了一个额外的平台来验证他们的算法。实验结果表明，与现有的方法相比，该模型有很大的提高。
Scene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches.