Unsupervised deep hashing through learning soft pseudo label for remote sensing image retrieval

Abstract

Unsupervised hashing is an important approach for large-scale content-based remote sensing (RS) image retrieval. Existing unsupervised hashing methods usually utilize data clustering to generate pseudo labels as supervised signals. In the RS domain, images of each class cannot be accurately grouped into individual clusters because image features are not well characterized by the pre-trained models learned on a nature image dataset. As a result, to preserve the similarity of images sharing at least one class, intra-cluster and inter-cluster similarities need to be carefully learned. However, existing pseudo-labels are hard pseudo-labels represented by scalar values, which cannot well reflect the semantic distance between inter-cluster images or the semantic distance between intra-cluster images. To address these problems, this paper proposes a soft-pseudo-label-based unsupervised deep hashing method for content-based RS image retrieval, called SPL-UDH. Soft pseudo-labels can accurately describe the global similarity between inter-cluster images by binarized vectors. Specifically, we design a deep auto-encoder network to learn soft pseudo-labels automatically and meanwhile to generate a local similarity matrix representing the proximity between intra-cluster images. Based on soft pseudo-labels and local similarity matrix, we propose a deep hashing network to simultaneously learn the inter-cluster similarity and the intra-cluster similarity between RS images. Moreover, we design a new objective function based on Bayesian theory so that the deep hashing network can be trained by jointly learning the soft pseudo-labels and the local similarity matrix. Extensive experiments on public RS image retrieval datasets demonstrate that SPL-UDH outperforms various state-of-the-art unsupervised hashing methods.