  The development of cross-modal retrieval systems that can search and retrieve
semantically relevant data across different modalities based on a query in any
modality has attracted great attention in remote sensing (RS). In this paper,
we focus our attention on cross-modal text-image retrieval, where queries from
one modality (e.g., text) can be matched to archive entries from another (e.g.,
image). Most of the existing cross-modal text-image retrieval systems in RS
require a high number of labeled training samples and also do not allow fast
and memory-efficient retrieval. These issues limit the applicability of the
existing cross-modal retrieval systems for large-scale applications in RS. To
address this problem, in this paper we introduce a novel unsupervised
cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS.
To this end, the proposed DUCH is made up of two main modules: 1) feature
extraction module, which extracts deep representations of two modalities; 2)
hashing module that learns to generate cross-modal binary hash codes from the
extracted representations. We introduce a novel multi-objective loss function
including: i) contrastive objectives that enable similarity preservation in
intra- and inter-modal similarities; ii) an adversarial objective that is
enforced across two modalities for cross-modal representation consistency; and
iii) binarization objectives for generating hash codes. Experimental results
show that the proposed DUCH outperforms state-of-the-art methods. Our code is
publicly available at https://git.tu-berlin.de/rsim/duch.
