Remote Sensing Image Recommendation Method Based on Multi-feature Fusion Neural Network
Considering the problem that manual query and ordering methods used by remote sensing users to obtain remote sensing images are relatively inefficient,a personalized recommendation framework for remote sensing images based on multi-feature fusion neural network is proposed.Firstly,this method designs a remote sensing image semantic system to realize image semantic extraction,and combines user operation records and responsibility description texts to construct a remote sensing knowledge graph.Subsequently,multi-dimensional attribute characteristics of users and images are extracted by embedding characterization methods.Finally,a multi-feature fusion neural network based on collaborative filtering is designed.With the help of neural network's high-dimensional spatial modeling capability,the proposed model achieves effective fusion of multi-dimensional attribute features of users and images,and obtains a better matching recommendation effect.Experimental results show the significant improvement of the proposed method compared with previous recommendation methods,which effectively improves the accuracy and timeliness of remote sensing services.
active recommendationremote sensing imagefeature fusionneural network
万方数据
维普
