Remote sensing scene classification based on attention network scale feature fusion
Aiming at the existence of remote sensing data sets within the class difference between large and class high similarity features in remote sensing scene classification accuracy is not high,a remote sensing image scene classification model based on attention network scale feature fusion(MSA-CNN).The remote sensing image is input to the VGG-16 network through scaling transformation is proposed to extract the multi-scale features of the remote sensing image.The multi-scale target region of the image is extracted by using the multi-scale attention model(MS-APN),and the target region is cut and enlarged and input into the three-layer network structure.The multi-scale features of the original image and the features of the target region are integrated,and LBP is used to express the global features to overcome the difference of the remote sensing image caused by different shooting angles.The fused multi-scale features are input to the full connection layer of the network to complete the final classification and prediction task.The experimental results show that the average classification accuracy of MSA-CNN is 1.63%and 2.66%higher than that of the attention circular convolutional network(ARCNet)and the traditional fine-grained circular attention network(RA-CNN)on the NWPU-Resisc45 public dataset,respectively.In the open data set of UC Merced Land-Use,it is 0.64%higher than that of RA-CNN.The results show that the proposed MSA-CNN can effectively improve the accuracy of scene classification of remote sensing images.
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