Remote Sensing Image Description Method Based on Improved CNN and GRU
Generating readable text descriptions for remote sensing images is of great significance for realizing automatic interpretation of remote sensing images and efficient mining of remote sensing image information.The fully connected layer of the traditional convolutional neural network(CNN)can only accept fixed inputs,remote sensing images with variable scales and complex backgrounds can lead to unnecessary loss of accuracy.We proposed a model that combines an improved CNN with a gated recurrent unit(GRU).The atrous spatial pyramid pooling(ASPP)module of feature weighted fusion is added to the CNN,which can be used to improve the accuracy of the model without losing resolution.In order to evaluate the performance,experiments were conducted on two public data sets,RSICD and UCM_Captions.The results show that the model performs well on the task of generating remote sensing image descriptions,and the generated natural language descriptions are more accurate,which can effectively improve the automation and semantic accuracy of remote sensing image analysis.
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