Remote sensing image recognition based on dual-channel deep learning network

In the face of remote sensing images with diversified information, the recognition of remote sensing images only through local features or global features is limited. Traditionally, it is difficult to achieve good image modeling. To solve this problem, this paper proposes a recognition framework based on dual-channel deep learning(DCDL). The purpose of this framework is to mine global feature information and local feature information at the same time. On the first channel, this paper uses a multi-scale convolution residual network to perform local mining and residual calculations on the image to generate local features; secondly, the local attention mechanism is used to assign weight coefficients to the local features to make the information more contained. More features are more prominent; finally, after several times of mining features and assigning weights, more representative deep features are produced. On the other channel, we introduce the global attention mechanism to realize the weight coefficient distribution of global features; then use the multi-scale dilated convolution to expand the receptive field to obtain a larger range of feature information; then, use the Sigmoid function to achieve 0 to 1 for all features The weight distribution of, further expands the difference between global features; finally, the deep mining of features is realized through 2-layer convolution. In this paper, through the experimental results of the three sub-data sets in the NWPU-RESISC45 data set, we can see that our proposed algorithm has achieved higher recognition accuracy.