  Due to the availability of large-scale multi-modal data (e.g., satellite
images acquired by different sensors, text sentences, etc) archives, 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 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 require a high number
of labeled training samples and also do not allow fast and memory-efficient
retrieval due to their intrinsic characteristics. 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
deep unsupervised cross-modal contrastive hashing (DUCH) method for RS
text-image retrieval. The proposed DUCH is made up of two main modules: 1)
feature extraction module (which extracts deep representations of the
text-image modalities); and 2) hashing module (which learns to generate
cross-modal binary hash codes from the extracted representations). Within the
hashing module, we introduce a novel multi-objective loss function including:
i) contrastive objectives that enable similarity preservation in both intra-
and inter-modal similarities; ii) an adversarial objective that is enforced
across two modalities for cross-modal representation consistency; iii)
binarization objectives for generating representative hash codes. Experimental
results show that the proposed DUCH outperforms state-of-the-art unsupervised
cross-modal hashing methods on two multi-modal (image and text) benchmark
archives in RS. Our code is publicly available at
https://git.tu-berlin.de/rsim/duch.
