  Image-text retrieval in remote sensing aims to provide flexible information
for data analysis and application. In recent years, state-of-the-art methods
are dedicated to ``scale decoupling'' and ``semantic decoupling'' strategies to
further enhance the capability of representation. However, these previous
approaches focus on either the disentangling scale or semantics but ignore
merging these two ideas in a union model, which extremely limits the
performance of cross-modal retrieval models. To address these issues, we
propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote
sensing image-text retrieval. Specifically, we design the Bidirectional Scale
Decoupling (BSD) module, which exploits Salience Feature Extraction (SFE) and
Salience-Guided Suppression (SGS) units to adaptively extract potential
features and suppress cumbersome features at other scales in a bidirectional
pattern to yield different scale clues. Besides, we design the Label-supervised
Semantic Decoupling (LSD) module by leveraging the category semantic labels as
prior knowledge to supervise images and texts probing significant
semantic-related information. Finally, we design a Semantic-guided Triple Loss
(STL), which adaptively generates a constant to adjust the loss function to
improve the probability of matching the same semantic image and text and
shorten the convergence time of the retrieval model. Our proposed SSJDN
outperforms state-of-the-art approaches in numerical experiments conducted on
four benchmark remote sensing datasets.
