Unsupervised Hash Fusion Method for Remote Sensing Image Retrieval
Because the scale of remote sensing image data is enormous and most of the data is not annotated,the unsupervised hashing algorithms that independent of data annotation are more suitable for remote sensing image retrieval.In this paper,a fusion method for unsupervised hashing was proposed.First,the manifold similarity between pre-trained features of remote sensing images was obtained by the random walk algorithm,and the similarity indication matrix was constructed by combining the cosine similarity between features,which could measure the validity of the unsupervised hash codes.Then,the validity of the hash code was used as a node,and the sorting correlation between hash codes was used as an edge to dynamically construct an association graph.The connected components in the graph would be adopted as the combination of hash codes,avoiding those hash code combinations that may result in degradation and reducing the computational complexity.Finally,using the normalized validity of the hash codes as a weight,adaptive late fusion was carried out with each combination scheme,which could promote the hash codes with stronger discrimination ability generating.A series of experiments on two datasets show that the proposed method could adaptively select suitable fusion schemes,effectively improve the retrieval performance of fused hash codes,and obtain fusion schemes with retrieval performance close to that of exhaustive methods at a lower training cost.
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