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.

本文针对遥感图像 - 文本检索的问题，提出一种新颖的 Scale-Semantic Joint Decoupling Network（SSJDN）方法，其中利用 Bidirectional Scale Decoupling（BSD）和 Label-supervised Semantic Decoupling（LSD）模块分别来处理尺度和语义解耦。同时，采用语义引导的三元损失（STL）来提高匹配概率和缩短检索时间。实验结果表明，所提出的方法在四个基准遥感数据集上优于当前最先进的方法。