Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, current RSCTIR methods mainly focus on global features of RS images, which leads to the neglect of local features that reflect target relationships and saliency. In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels. MIDF leverages local information to correct global information, utilizes global information to supplement local information, and uses the dynamic addition of the two to generate prominent visual representation. To alleviate the pressure of the redundant targets on the graph convolution network (GCN) and to improve the model’s attention on salient instances during modeling local features, the denoised representation matrix and the enhanced adjacency matrix (DREA) are devised to assist GCN in producing superior local representations. DREA not only filters out redundant features with high similarity, but also obtains more powerful local features by enhancing the features of prominent objects. Finally, to make full use of the information in the similarity matrix during inference, we come up with a plug-and-play multivariate rerank (MR) algorithm. The algorithm utilizes the$k$nearest neighbors of the retrieval results to perform a reverse search, and improves the performance by combining multiple components of bidirectional retrieval. Extensive experiments on public datasets strongly demonstrate the state-of-the-art performance of GaLR methods on the RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files have been made available at:https://github.com/xiaoyuan1996/GaLR.

The encoder–decoder framework is prevalent in existing remote-sensing image captioning (RSIC) models. The appearance of attention mechanisms brings significant results. However, current attention-based caption models only build up the relationships between the local features without introducing the global visual feature and removing redundant feature components. It will cause caption models to generate descriptive sentences that are weakly related to the scene of images. To solve the problems, this article proposed a global visual feature-guided attention (GVFGA) mechanism. First, GVFGA introduces the global visual feature and fuses them with local visual features to build up their relationships between them. Second, an attention gate utilizing the global visual feature is proposed in GVFGA to filter out redundant feature components in the fused image features and provide more salient image features. In addition, to relieve the hidden state’s burden, a linguistic state (LS) is proposed to specifically provide textual features, making the hidden state only guiding visual–textual attention process. What’s more, to further refine the fusion of visual features and textual features, a LS-Guided Attention (LSGA) mechanism is proposed. It can also filter out the irrelevant information in the fused visual–textual feature with the help of an attention gate. The experimental results show that this proposed image captioning model can achieve better results on three RSIC datasets, UCM-Captions, Sydney-Captions, and RSICD datasets.

It is widely shared that capturing relationships among multi-modality features would be helpful for representing and ultimately describing an image. In this paper, we present a novel Intra- and Inter-modality visual Relation Transformer to improve connections among visual features, termed I2RT. Firstly, we propose Relation Enhanced Transformer Block (RETB) for image feature learning, which strengthens intra-modality visual relations among objects. Moreover, to bridge the gap between inter-modality feature representations, we align them explicitly via Visual Guided Alignment (VGA) module. Finally, an end-to-end formulation is adopted to train the whole model jointly. Experiments on the MS-COCO dataset show the effectiveness of our model, leading to improvements on all commonly used metrics on the "Karpathy" test split. Extensive ablation experiments are conducted for the comprehensive analysis of the proposed method.