With the emergence of large-scale vision-language pre-training (VLP) models, remote sensing (RS) image–text retrieval is shifting from global representation learning to fine-grained semantic alignment. This review systematically examines two mainstream representation paradigms—real-valued embedding and deep hashing—and analyzes how the evolution of RS datasets influences model capability, including multi-scale robustness, small object discriminability, and temporal semantic understanding. We further dissect three core challenges specific to RS scenarios: multi-scale semantic modeling, small object feature preservation, and multi-temporal reasoning. Representative architectures and technical solutions are reviewed in depth, followed by a critical discussion of their limitations in terms of generalization, evaluation consistency, and reproducibility. We also highlight the growing role of VLP-based models and the dependence of their performance on large-scale, high-quality image–text corpora. Finally, we outline future research directions, including RS-oriented VLP adaptation and unified multi-granularity evaluation frameworks. These insights aim to provide a coherent reference for advancing practical deployment and promoting cross-domain applications of RS image–text retrieval.

The research background of military communication network starts from the early period of human society. Affected by the continuous development of science and technology, military communication plays an increasingly prominent role in war. In today's society, military communication network has serious error rate and low efficiency, which has great influence on military information transmission. In this paper, the military communication network system with K-means algorithm is adopted, and the conclusion is drawn through experiments. The algorithm greatly improves the accuracy and efficiency of military communication networks, increasing the accuracy rate to 99.6%.

In remote sensing image–text retrieval (RSITR), precise cross-modal retrieval is often hindered by centroid drift and ambiguous decision boundaries caused by high inter-class visual similarity. To address these bottlenecks, this study proposes a Category-aware Global–Local Semantic Alignment (CGLSA) framework fine-tuned on the CLIP (ViT-B/16) backbone. The architecture orchestrates two complementary mechanisms: Global Semantic Collaborative Alignment that regularizes macro-level category centroids using momentum updates and Bayesian prior calibration, and Local Fine-grained Feature Alignment that refines instance-level matching via dynamic scale adjustment and category-aware topological masks. Extensive evaluations on three major benchmark datasets (RSICD, RSITMD, and UCM-Captions) validate the model’s efficacy. Compared to strictly controlled CLIP-family baselines under equivalent supervised conditions, CGLSA achieves new state-of-the-art performance across all R@K metrics and mean recall. Extensions adapting this robust centroid formation to semi-supervised and open-vocabulary scenarios are identified for future work.