Concerning the problem that the local feature and rotation invariant feature cannot be jointly paid attention to in traditional attention-based neural networks, a multi-branch neural network model based weakly supervised fine-grained image classification method was proposed. Firstly, the lightweight Class Activation Map (CAM) network was utilized to localize the local region with potential semantic information, and the residual network ResNet-50 with deformable convolution and Oriented Response Network (ORN) with rotation invariant coding were designed. Secondly, the pre-trained model was employed to initialize the feature networks respectively, and the original image and the above regions were input to fine-tune the model. Finally, the three intra-branch losses and between-branch losses were combined to optimize the entire network, and the classification and prediction were performed on the test set. The proposed method achieves the classification accuracies of 87.7% and 90.8% on CUB-200-2011 dataset and FGVC_Aircraft dataset respectively, which are increased by 1.2 percentage points, and 0.9 percentage points respectively compared with those of the Multi-Attention Convolutional Neural Network (MA-CNN) method. On Aircraft_2 dataset, the proposed method reaches 91.8% classification accuracy, which is 4.1 percentage points higher than that of ResNet-50. The experimental results show that the proposed method improves the accuracy of weakly supervised fine-grained image classification effectively.

The Convolutional Neural Network (CNN) treats the potential object information and background information equally in the input image. However, there are many small objects and complex background in remote sensing scene images. To solve the problem above, a scale-attention network was proposed based on attention mechanism and multi-scale feature transformation. Firstly, a fast and effective attention module was developed, and the attention map was generated based on optimal feature selection. Then, with the attention map embedded, the multi-scale feature fusion layer added and the fully connected layer redesigned on the basis of ResNet50 network, a scale attention network was proposed. Secondly, the pre-training model was used to initialize the scale-attention network, and the training set was employed for the fine-tuning of the network. Finally, the fine-tuned scale-attention network was used to realize the classification prediction of test set. The classification accuracy of the proposed method on the AID scene dataset is 95.72%, which is 2.62 percentage points higher than that of ArcNet. On the NWPU-RESISC scene dataset, this method achieves classification accuracy of 92.25%, 0.95 percentage points higher than that of IORN （Improved Oriented Response Network）. The experimental results demonstrate that the proposed method is able to improve the classification accuracy of remote sensing image scenes.