Most of the current capsule network methods improve the classification accuracy by modifying iterative routing， while ignoring the burden brought by complex computation of iterative routing itself. Although there are some methods that use non-iterative routing to train the capsule network， the accuracies of these methods are not good. To address the above problem， a non-iterative routing graph capsule network method for remote sensing scene classification was proposed. Firstly， the preliminary features of the input image were extracted using a simple convolutional layer. Then， by performing dual attention between channels and capsules sequentially， a global attention module with dual fusion between channels and capsules was presented to generate global coefficients that weighed high-level capsule features. As a result， the weighted high-level capsule features became more discriminative to highlight the important capsules， thereby improving the classification performance. Meanwhile， an equivariant regularization term that could compute the similarity among the input images was introduced to model the explicit equivariance of the capsule network， thereby improving network performance potentially. Finally， the whole network was trained based on the loss function combining margin loss and equivariance loss to obtain a discriminative classification model. Experimental results on multiple benchmark scene datasets verified the effectiveness and efficiency of the proposed method. Experimental results show that the proposed method has the classification accuracy reached 90.38% on Canadian Institute For Advanced Research-10 image datasets （CIFAR-10）， which is 15.74 percentage points higher than the Dynamic Routing Capsule network （DRCaps） method， and achieves classification accuracy of 98.21% and 86.96% on Affine extended National Institute of Standards and Technology dataset （AffNIST） and Aerial Image Dataset （AID）， respectively. It can be seen that the proposed method can improve the performance of remote sensing scene classification effectively.

At present， most pooling methods mainly extract aggregated feature information from the 1-order pooling layer or the 2-order pooling layer， ignoring the comprehensive representation capability of multiple pooling strategies for scenes， which affects the scene recognition performance. To address the above problems， a joint model with first- and second-order pooling networks learning for remote sensing scene classification was proposed. Firstly， the convolutional layers of residual network ResNet-50 were utilized to extract the initial features of the input images. Then， a second-order pooling approach based on the similarity of feature vectors was proposed， where the information distribution of feature values was modulated by deriving their weight coefficients from the similarity between feature vectors， and the efficient second-order feature information was calculated. Meanwhile， an approximate solving method for calculating square root of covariance matrix was introduced to obtain the second-order feature representation with higher semantic information. Finally， the entire network was trained with the combination loss function composed of cross-entropy and class-distance weighting. As a result， a discriminative classification model was achieved. The proposed method was tested on AID （50% training proportion）， NWPU-RESISC45 （20% training proportion）， CIFAR-10 and CIFAR-100 datasets and achieved classification accuracies of 96.32%， 93.38%， 96.51% and 83.30% respectively， which were increased by 1.09 percentage points， 0.55 percentage points， 1.05 percentage points and 1.57 percentage points respectively， compared with iterative matrix SQuare RooT normalization of COVariance pooling （iSQRT-COV）. Experimental results show that the proposed method effectively improves the performance of remote sensing scene classification.