Abstract: Zero-shot classification algorithm does not need to label the sample of unseen classes to be recognized,so it can greatly reduce the cost of practical application,which has attracted wide attention in recent years.The problem of structure difference between word vectors and image feature prototypesseriously affects the zero-shot classification performance of remote sensing scenes.Based on the complementarity among different kinds of word vectors,the remote sensing scenes zero-shot classification algorithm based on word vectors fusion,named coupled analysis dictionary lear-ning method,was proposed.Firstly,the sparse coefficients of different kinds of word vectors are obtained by the more efficient analysis dictionary learning to reduce the redundant information.Then,the sparse coefficients are concatenated and denoted as the fused word vectors,and a structure alignment operation is performed based on the image feature prototypes to reduce structural differences by embedding the fused word vectors into image feature space.Finally,the image feature prototypes of the scene classes unseen are calculated,and the nearest neighbor classifier is employed to complete the classification in the image feature space.Quantitative experiments of the fusion of multiple semantic word vectors were carried out on UCM and AID datasets.At the same time,two real remote sensing images were qualitatively tested with RSSCN7 datasets as the seen dataset.Auantitative experiments obtaines the highest overall classification accuracies of 48.40% and 60.23% on UCM and AID,which respectively exceeds the typical comparative methods by 4.80% and 6.98%.In qualitative experiments on two real remote sensing images ,the algorithm also obtaines the best zero-shot classification performance.The experimental results show that the fused word vectors are more consistent with the prototypes in image feature space,and the zero-shot classification accuracies of remote sensing scenes can be significantly improved.

Key words: Analysis dictionary learning, Remote sensing scenes classification, Structure alignment, Word vectors fusion, Zero-shot classification