# Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information

arXiv: https://arxiv.org/abs/2204.09860 · HTML: https://mlsanity.com/paper/2204.09860/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [Geom-GCN: Geometric Graph Convolutional Networks](https://mlsanity.com/paper/2002.05287/) (2020)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [Deep Modular Co-Attention Networks for Visual Question Answering](https://mlsanity.com/paper/1906.10770/) (2019)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [Large-Scale Learnable Graph Convolutional Networks](https://mlsanity.com/paper/1808.03965/) (2018)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval](https://mlsanity.com/paper/1909.05506/) (2019)
- [Learning Semantic Concepts and Order for Image and Sentence Matching](https://mlsanity.com/paper/1712.02036/) (2017)
- [PP-YOLO: An Effective and Efficient Implementation of Object Detector](https://mlsanity.com/paper/2007.12099/) (2020)
- [Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.06133/) (2019)
- [Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking](https://mlsanity.com/paper/1908.04011/) (2019)
- [SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioning](https://mlsanity.com/paper/2006.08432/) (2020)
- [CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing](https://mlsanity.com/paper/1904.04794/) (2019)

## Built on by (most cited first, 30 of 40)

- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [RSGPT: A Remote Sensing Vision Language Model and Benchmark](https://mlsanity.com/paper/2307.15266/) (2023)
- [RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing](https://mlsanity.com/paper/2306.11300/) (2023)
- [SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing](https://mlsanity.com/paper/2312.12856/) (2023)
- [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://mlsanity.com/paper/2305.05726/) (2023)
- [Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks](https://mlsanity.com/paper/2208.04441/) (2022)
- [Large Language Models for Captioning and Retrieving Remote Sensing Images](https://mlsanity.com/paper/2402.06475/) (2024)
- [Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2410.08613/) (2024)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2308.12509/) (2023)
- [Direction-Oriented Visual-semantic Embedding Model for Remote Sensing Image-text Retrieval](https://mlsanity.com/paper/2310.08276/) (2023)
- [Composed Image Retrieval for Remote Sensing](https://mlsanity.com/paper/2405.15587/) (2024)
- [Learning to Evaluate Performance of Multi-modal Semantic Localization](https://mlsanity.com/paper/2209.06515/) (2022)
- [Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning](https://mlsanity.com/paper/2412.19179/) (2024)
- [UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation](https://mlsanity.com/paper/2404.14241/) (2024)
- [Knowledge-aware Text-Image Retrieval for Remote Sensing Images](https://mlsanity.com/paper/2405.03373/) (2024)
- [Multilingual Vision-Language Pre-training for the Remote Sensing Domain](https://mlsanity.com/paper/2410.23370/) (2024)
- [DOFA-CLIP: Multimodal Vision-Language Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.06312/) (2025)
- [Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing](https://mlsanity.com/paper/2212.05752/) (2022)
- [CroBIM-U: Uncertainty-Driven Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2601.03490/) (2026)
- [PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2405.10160/) (2024)
- [Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2405.18959/) (2024)
- [Dynamic Visual Semantic Sub-Embeddings and Fast Re-Ranking](https://mlsanity.com/paper/2309.08154/) (2023)
- [From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing](https://mlsanity.com/paper/2411.05826/) (2024)
- [Self-Supervised Cross-Modal Text-Image Time Series Retrieval in Remote Sensing](https://mlsanity.com/paper/2501.19043/) (2025)
- [Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment](https://mlsanity.com/paper/2404.18253/) (2024)
- [Cross-Modal Pre-Aligned Method with Global and Local Information for Remote-Sensing Image and Text Retrieval](https://mlsanity.com/paper/2411.14704/) (2024)
- [FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing](https://mlsanity.com/paper/2504.16433/) (2025)
- [HCCM: Hierarchical Cross-Granularity Contrastive and Matching Learning for Natural Language-Guided Drones](https://mlsanity.com/paper/2508.21539/) (2025)
- [LuoJiaHOG: A Hierarchy Oriented Geo-aware Image Caption Dataset for Remote Sensing Image-Text Retrival](https://mlsanity.com/paper/2403.10887/) (2024)

## Abstract (arXiv)

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 de-noised 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 .