The existing convolutional neural network (CNN) models have shown excellent performance in remote sensing scene classification. However, the structure of such models is becoming more and more complex, and the learning of low-level features is difficult to interpret. To address this problem, in this study, we introduce lie group machine learning into the CNN model, try to combine both approaches to extract more distinguishing ability and effective features, and propose a novel network model, namely, the lie group regional influence network (LGRIN). First, manifold space samples of the lie group are obtained by mapping, and then, the features of the lie group are extracted after the operations of image decomposition and integral image calculation. Second, the multidilation pooling is integrated into the CNN architecture. At the same time, the image regional influence network module is designed to guide the attention of the classification model by using the regional-level supervision of the decomposition. Finally, the fusion features are classified, and the predicted results are obtained. Our model takes full advantage of regional influence, lie group kernel function, and lie group feature learning. Moreover, our model produces satisfactory performance on three public and challenging data sets: Aerial Image Dataset (AID), UC Merced, and NWPU-RESISC45. The experimental results verify that, compared with the state-of-the-art methods, this method is more explanatory and achieves higher accuracy.

Discriminative feature learning is the key to remote sensing scene classification. Previous research has found that most of the existing convolutional neural networks (CNN) focus on the global semantic features and ignore shallower features (low-level and middle-level features). This study proposes a novel Lie Group deep learning model for remote sensing scene classification to solve the above-mentioned challenges. Firstly, we extract shallower and higher-level features from images based on Lie Group machine learning (LGML) and deep learning to improve the feature representation ability of the model. In addition, a parallel dilated convolution, a kernel decomposition, and a Lie Group kernel function are adopted to reduce the model’s parameters to prevent model degradation and over-fitting caused by the deepening of the model. Then, the spatial attention mechanism can enhance local semantic features and suppress irrelevant feature information. Finally, feature-level fusion is adopted to reduce redundant features and improve computational performance, and cross-entropy loss function based on label smoothing is used to improve the classification accuracy of the model. Comparative experiments on three public and challenging large-scale remote-sensing datasets show that our model improves the discriminative ability of features and achieves competitive accuracy against other state-of-the-art methods.

The key problem of remote sensing image classification is to understand the semantic content of the images effectively. Up to now, most methods are to improve the accuracy of the final classification through the convolutional neural networks model. However, the structure of the whole model is complex and contains a large number of parameters. To address this problem, in this study, we proposed a lightweight method of the intrinsic mean within Lie groups, which can achieve high accuracy. We apply the computational advantage of Lie group manifold space and design the kernel function that is suitable for both Lie group sample and vector sample. Experiments are performed on two publicly available and challenging data sets [aerial image dataset (AID) and Northwestern Polytechnical University-REmote Sensing Image Scene Classification, contains 45 scene classes (NWPU-RESISC45)], and the results show that our method is more accurate than the state-of-the-art methods.

Remote sensing scene classification is used to label specific semantic categories for images. The current methods have achieved competitive performances, but they are only for Euclidean space samples. As a result, their representations are not robust for non-Euclidean space samples, which affects the classification accuracy. In this letter, we introduce the Lie group manifold into the traditional feature representation method and propose a novel intrinsic mean representation method within the Lie group. At the same time, the kernel function based on the sample of the Lie group is designed to further improve the robustness and accuracy of classification. In addition, our method achieves satisfactory performance on two public and challenging remote sensing data sets of UC Merced and NWPU-RESISC45.