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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. [ABSTRACT FROM AUTHOR]