Recently, many state-of-the-art results for remote sensing image scene classification have been achieved by convolutional neural networks (CNNs) due to their large learning capability. However, in the forward process of CNNs, the high-frequency/texture features are gradually blurred with hierarchical downsampling and convolution operations. High-frequency features are important to capture the diversity within a class and the similarity between classes. For example, the line features are crucial to distinguish a tennis court from a basketball court. For tennis court in different scenes, the highlight of line features can effectively avoid the influence of diverse backgrounds. As a consequence, we propose a Laplacian high-frequency convolutional block (LHCB) based on CNN to extract useful high-frequency features by trainable Laplacian operator. To propagate high-frequency features, we embed LHCB into the existing CNN structures and obtain LHNet. In LHNet, there are two pathways. The original CNN architecture can be taken as the low-frequency pathway, and we propose a high-frequency pathway (HFP) based on LHCB that propagates the residual high-frequency features blurred in each low-frequency layer. Considering that the high-frequency features usually show large variance between images of the same class, we propose a new objective for HFP to enhance the intraclass similarity of high-frequency features. The final objective function is obtained by combining the new objective and the baseline classification objective. Numerous experiments on three public available remote sensing image scene classification datasets, the Northwestern Polytechnical University-Remote Sensing Image Scene Classification (NWPU-RESISC45), Aerial Image Dataset (AID), and the University of California at Merced (UC Merced), demonstrate the superior performance of the proposed method.

中文翻译：

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LHNet：用于遥感图像场景分类的拉普拉斯卷积块


最近，由于卷积神经网络（CNN）具有强大的学习能力，在遥感图像场景分类方面取得了许多最先进的成果。然而，在CNN的前向过程中，随着分层下采样和卷积操作，高频/纹理特征逐渐模糊。高频特征对于捕获类内的多样性和类之间的相似性非常重要。例如，线条特征对于区分网球场和篮球场至关重要。对于不同场景的网球场，突出线条特征可以有效避免不同背景的影响。因此，我们提出了一种基于 CNN 的拉普拉斯高频卷积块（LHCB），通过可训练的拉普拉斯算子提取有用的高频特征。为了传播高频特征，我们将 LHCB 嵌入到现有的 CNN 结构中并获得 LHNet。在 LHNet 中，有两条路径。原始的 CNN 架构可以视为低频路径，我们提出了一种基于 LHCB 的高频路径（HFP），它传播每个低频层中模糊的残余高频特征。考虑到高频特征通常在同一类图像之间表现出较大的差异，我们提出了 HFP 的新目标来增强高频特征的类内相似性。最终的目标函数是通过将新目标和基线分类目标相结合得到的。 对三个公共可用的遥感图像场景分类数据集（西北工业大学遥感图像场景分类（NWPU-RESISC45）、航空图像数据集（AID）和加州大学默塞德分校（UC Merced）进行的大量实验证明了所提出方法的优越性能。