## 1 Introduction  
With the rapid development of remote sensing observation technology over the past few years, the data size of RS images is increasing [1]. The question of how to quickly and accurately access effective information from large amounts of RS data has become a challenge, gaining widespread attentions from numerous researchers [2; 3; 4]. This technology has broad prospects in disaster relief, disaster forewarning, resource management, and more fields [5; 6; 7]. From the data modality involved in retrieval tasks, RS image retrieval can be classified into two modes, single-modal retrieval and cross-modal retrieval [8]. In single-modal retrieval, both query data and archived data are RS images, and existing research methods are mostly concentrated on CBIR [9], whose accuracy has been promoted tremendously, owing to the application and development of deep metric learning over the past few years [10; 11; 12; 13]. At the same time, the application of the deep Hash method has made large-scale RS image retrieval possible [14; 15; 16; 17]. While single-modal retrieval has achieved enormous progress, it has natural weaknesses in retrieval flexibility and semantic richness. Thus, cross-modal retrieval has evolved as a recent appeal to researchers [18]. The so-called cross-modal retrieval means the query data and the target data pertain to separate modalities (e.g., searching images with texts or voice). Cross-modal retrieval is typically more challenging than single-modal retrieval--because there are heterogeneous gaps between data from different modalities [19; 20], making it hard to maintain the semantic alignment relation. In fact, cross-modal retrieval has achieved satisfactory accuracy in the field of natural images [21; 22]. Therefore, predecessors have made in-depth explorations into the distinction of cross-modal retrieval in the two fields. [7] suggests the multiple scales, small targets, high resolution, and lack of annotation information of RS images are to blame for impeding effective cross-modal retrieval. [18] further summarizes three major challenges in this field: a large number of redundant features in RS images, high intraclass similarity, and the coarse semantic granularity of traditional RS image datasets. Aiming against the above issues, a few cross-modal RS image retrieval methods have been proposed in the past three years. (1) Semantic alignment. To fix the problem of semantic alignment, ref [9] designed a novel multi-objective loss function that maintains the semantic alignment relation between images and texts via adversarial loss. Ref. [23] proposed an adversarial learning-guided multilabel attention mechanism to ensure semantic association between modalities and combine the Hash module with asymmetric loss, cosine triplet margin loss, and cosine quantization loss so as to give higher similarity to the Hash code of sample couples. An image-sound retrieval model SCRL has been proposed, which narrows heterogeneous semantic gaps by simultaneously modeling the relation among cross-modal paired data, cross-modal nonpaired data, and single-modal data [24]. Researchers [19] have also designed an effective triplet choice function, with the hardest negative example to constrain cross-modal semantic alignment. All the above methods proceed from improving the objective function, trying to model more compact cross-modal semantic associations, and constraining network training, and they have achieved good effects. (2) Redundant features. In addition, focusing on the issues of multiple scales and redundant features of RS images, [18] designed an asymmetric multimodal matching network (AMFMN), which not only accommodated multiscale feature input but also allowed filtering redundant features out of images via a multiscale visual self-attention (MVSA) module.