There are large differences in shape, texture and color of images in remote sensing image datasets, and the classification accuracy of remote sensing scenes is low due to the scale differences cased by different shooting heights and angles. Therefore, a Feature Aggregation Compensation Convolution Neural Network (FAC-CNN) was proposed, which used active rotation aggregation to fuse features of different scales and improved the complementarity between bottom features and top features through bidirectional gated method. In the network, the image pyramid was used to generate images of different scales and input them into the branch network to extract multi-scale features, and the active rotation aggregation method was proposed to fuse features of different scales, so that the fused features have directional information, which improved the generalization ability of the model to different scale inputs and different rotation inputs, and improved the classification accuracy of the model. On NorthWestern Polytechnical University REmote Sensing Image Scene Classification (NWPU-RESISC) dataset, the accuracy of FAC-CNN was increased by 2.05 percentage points and 2.69 percentage points respectively compared to those of Attention Recurrent Convolutional Network based on VGGNet (ARCNet-VGGNet) and Gated Bidirectional Network (GBNet); and on Aerial Image Dataset (AID), the accuracy of FAC-CNN was increased by 3.24 percentage points and 0.86 percentage points respectively compared to those of the two comparison networks. Experimental results show that FAC-CNN can effectively solve the problems in remote sensing image datasets and improve the accuracy of remote sensing scene classification.

In order to overcome the difficulty of visual feature extraction and meet the real-time recognition demand of Beijing Opera characters, a Convolutional Neural Network based on HyperColumn Attention(HCA-CNN) was proposed to extract and recognize the fine-grained features of Beijing Opera characters. The idea of HyperColumn features used for image segmentation and fine-grained positioning were applied to the attention mechanism used for key area positioning in the network. The multi-layer superposition features was formed by concatenating the backbone classification network in the forms of pixel points through the HyperColumn set, so as to better take into account both the early shallow spatial features and the late depth category semantic features, and improve the accuracy of positioning task and backbone network classification task. At the same time, the lightweight MobileNetV2 was adopted as the backbone network of the network, which better met the real-time requirement of video application scenarios. In addition, the BeiJing Opera Role(BJOR) dataset was created and the ablation experiments were carried out on this dataset. Experimental results show that, compared with the traditional fine-grained Recurrent Attention Convolutional Neural Network(RA-CNN), HCA-CNN not only improves the accuracy index by 0.63 percentage points, but also reduces the Memory Usage and Params by 162.84 MB and 131.5 MB respectively, and reduces the times of multiplication and addition Mult-Adds and floating-point operations per second FLOPs by 39 885×10 ⁶ times and 51 886×10 ⁶ times respectively. It verifies that the proposed HCA-CNN can effectively improve the accuracy and efficiency of Beijing Opera character recognition, and can meet the requirements of practical applications.

In order to fully use the structural information of the data, and compress the image data, the sparse constraints of the subspace (feedback) were applied to the object function of non-negative tensor factorization. Then this algorithm was used to reduce the dimension of the image sets. Finally, image classification was realized. The experimental results on the handwritten digital image database show that the proposed algorithm can effectively improve the accuracy of the image classification.