Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of changes in bi-temporal remote sensing images, providing more semantically interpretable results than conventional pixel-level change detection methods. However, existing approaches often rely on stacked Transformer modules, leading to suboptimal feature discrimination. Moreover, direct difference computation after feature extraction tends to retain task-irrelevant noise, limiting the model’s ability to capture meaningful changes. This study proposes a novel cross-spatial Transformer and symmetric difference localization network (CTSD-Net) for RSICC to address these limitations. The proposed Cross-Spatial Transformer adaptively enhances spatial-aware feature representations by guiding the model to focus on key regions across temporal images. Additionally, a hierarchical difference feature integration strategy is introduced to suppress noise by fusing multi-level differential features, while residual-connected high-level features serve as query vectors to facilitate bidirectional change representation learning. Finally, a causal Transformer decoder creates accurate descriptions by linking visual information with text. CTSD-Net achieved BLEU-4 scores of 66.32 and 73.84 on the LEVIR-CC and WHU-CDC datasets, respectively, outperforming existing methods in accurately locating change areas and describing them semantically. This study provides a promising solution for enhancing interpretability in remote sensing change analysis.

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.