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.