## 1 Introduction
Specifically, the MVSA module includes a multiscale feature fusion network, which is used to extract the joint semantic information between layers, as well as a redundant feature filter, which extracts the significant information from joint feature representation. [7] first proposed a deep semantic alignment network based on the gate mechanism and the attention mechanism, which has achieved a satisfactory result when used to strengthen the correspondence between image areas and words and filter out redundant information. The above two methods have achieved good results. (3) High intraclass similarity. To solve this problem, researchers [9] have implemented a more balanced distribution inside unimodal data by embedding a comparative learning module into each of two unimodal encoders. Particularly, they [9] demonstrated experimentally the method of vision enhancement in details, drew a reliable conclusion, and proposed a rule-based text augmentation method based on word replacement, where words are replaced by semantically similar ones. On the basis of a previous ablation study [9], the removal of the two comparative learning modules would impair the model's performance far more seriously than any other modification. [18] designed a triplet loss function with a dynamic variable margin, which changed the fixed margin in the traditional triplet loss based on sample pairs' prior similarity, easing the problem of high intraclass similarity of RS images. Although all the above methods have to some extent solved the puzzle of RS image cross-modal retrieval and enhanced the model's precision, it seems to us that this field remains confronted with the challenge of multi-scene semantics in RS images. We can interpret multi-scene semantics in the following way: Unlike natural images, which typically focus on a certain subject, RS images, as a reflection of objective things, contain richer and more obvious spatial information, geometric structure, textual information, and other details [25]. RS images are referred to as \"multi-scene semantic remote sensing images\" (or \"multilabel remote sensing images\" in [26]) because there are typically multiple objectives in them, which may be loosely correlated in semantic terms and belong to different scenes. This will result in the fact that, for a specific query, only some areas of the RS image are related to it, while the others will become \"redundant features\" disturbing the semantic association between the query data and the target data. Despite the description of \"redundant features\" by [18], it seems to us that the so-called \"redundancy\" is not invariable but closely associated with query--for instance, if the same image is queried using two different textual statements, then the \"redundant features\" will vary. Therefore, it may not be an optimal choice to filter out redundant features through mere image processing without considering the query data. Adopting the cross-attention mechanism, with textual semantics guiding the network to filter redundant features out of images, would probably turn out to be a feasible solution to this problem [27]. The principle of this solution is shown in Figure 1. Indeed, the attention mechanism has been widely applied in the field of cross-modal retrieval of natural images, and its feasibility has been proven by experimental results [27; 28; 29; 30]. Inspired by the above methods, this paper, focusing on the issue of mutual interference between multi-scene semantic information in RS images, puts forward a new model for cross-modal image-text retrieval from RS images, called the cross-attention model based on image area level semantic features (CABIR). To acquire region-level image features that are more adaptive to the cross-attention mechanism, we gave up the global average pooling layer common in convolution neural networks (CNNs) and instead adopted the output of the last convolution as the region-level semantic features of images. To acquire the statement-level features of texts, we designed a deep network of BERT plus Bi-GRU.