Remote sensing image captioning aims to describe the content of images using natural language. In contrast with natural images, the scale, distribution, and number of objects generally vary in remote sensing images, making it hard to capture global semantic information and the relationships between objects at different scales. In this paper, in order to improve the accuracy and diversity of captioning, a mask-guided Transformer network with a topic token is proposed. Multi-head attention is introduced to extract features and capture the relationships between objects. On this basis, a topic token is added into the encoder, which represents the scene topic and serves as a prior in the decoder to help us focus better on global semantic information. Moreover, a new Mask-Cross-Entropy strategy is designed in order to improve the diversity of the generated captions, which randomly replaces some input words with a special word (named [Mask]) in the training stage, with the aim of enhancing the model’s learning ability and forcing exploration of uncommon word relations. Experiments on three data sets show that the proposed method can generate captions with high accuracy and diversity, and the experimental results illustrate that the proposed method can outperform state-of-the-art models. Furthermore, the CIDEr score on the RSICD data set increased from 275.49 to 298.39.

Remote sensing image captioning (RSIC) aims to describe ground objects and scenes within remote sensing images in natural language form. As the complexity and diversity of scenes in remote sensing images increase, existing methods, although effective in specific tasks, are largely trained on particular scene images and corpora. This limits their ability to generate descriptions for scenes not encountered during training. Given the finite resources for data annotation and the expanding range of application scenarios, training data typically cover only a subset of common scenes, leaving many potential scene types unrepresented. Consequently, developing models capable of effectively handling unseen scenes with limited training data is imperative. This study introduces an innovative remote sensing image captioning model based on scene attribute learning—SALCap. The proposed model defines scene attributes and employs a specifically designed global object scene attribute extractor to capture these attributes. It then uses an attribute inference module to predict scene information through scene attributes, ensuring that this part of the scene’s information is reused in sentence generation through additional attribute loss. Experiments show that the method not only improves the accuracy of the description but also significantly enhances the model’s adaptability and generalizability relative to unseen scenes. This advancement expands the practical utility of remote sensing image captioning across diverse scenarios, particularly under the constraints of limited annotations.