Current remote sensing image captioning (RSIC) methods often struggle to provide accurate and comprehensive descriptions due to their reliance on networks designed for natural images. Due to limited domain-specific knowledge in remote sensing, these networks often fail to accurately reflect the intrinsic semantic information of remote sensing categories. This paper proposes a novel Knowledge-Embedded Remote Sensing Image Captioning model (KE-RSIC). We first define two types of remote sensing knowledge: general knowledge within the field of remote sensing, and specific knowledge that is relevant to the input image. To acquire general knowledge, we construct a remote sensing knowledge graph and propose a general knowledge embedding method, enabling semantic correlations between entities and relationships in remote sensing knowledge graphs. The generated entity embeddings and relationship embeddings can effectively capture the intrinsic semantic information of remote sensing categories. To acquire specific knowledge, we also propose a specific knowledge embedding method. We retrieve reports with similar label distributions to the input and then extract entities and relationships from the retrieved reports using a relation extractor. Embedding specific knowledge can alleviate to some extent the issue of poor matching between visual features and semantic features due to the lack of relevant knowledge. Subsequently, to integrate entity embeddings, relationship embeddings, and visual features, we propose a Visual Feature and Knowledge Information Dynamic Fusion module (VFKII). This module can efficiently combine the visual features of remote sensing images with structural information on embedded knowledge. Numerous experimental findings attest to the superiority and effectiveness of the proposed method.

In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation. For the purpose of encrypting data in practical applications, we propose a variety of data preprocessing methods for different data accuracies. We also propose data preprocessing strategies based on different data precision and different computational overheads. In addition, we also design a protocol to implement the cipher text comparison function between users and cloud servers. Analysis of experimental results indicates that our proposed scheme has high clustering accuracy and can guarantee the privacy and security of the data.

Remote sensing image captioning (RSIC) is an important task in environmental monitoring and disaster assessment. However, existing methods are constrained by redundant feature interference, insufficient multi-scale feature integration, and cross-modal semantic gaps, leading to limited performance in scenarios requiring fine-grained descriptions and semantic integrity, such as disaster assessment and emergency response. In this letter, we propose a Cross-Modal Semantic Alignment Model for Remote Sensing Image Captioning (CSA-RSIC), addressing these challenges with three innovations. First, we designed an Adaptive Feature Selection Module (AFSM) that generates channel weights through dual pooling. The AFSM dynamically weights the most informative features at each scale to improve caption accuracy. Second, we propose a Cross-Scale Feature Aggregation Module (CFAM) that constructs a hierarchical feature pyramid by aligning multi-scale resolutions and performs attention-guided fusion with enhanced weighting via AFSM, ensuring the effective integration of fine-grained and global semantic information. Finally, a novel loss function that combines contrastive learning and consistency loss is proposed to enhance the semantic alignment between visual and textual features. Experiments on three datasets show the advancement of CSA-RSIC over strong baselines, indicating its effectiveness in enhancing both semantic completeness and accuracy.

With the maturity of computer vision and natural language processing technology, we are becoming more ambitious in image captioning. In particular, we are more ambitious in generating longer, richer, and more accurate sentences as image descriptions. Most existing image caption models use an encoder—decoder structure, and most of the best-performing models incorporate attention mechanisms in the encoder—decoder structure. However, existing image captioning methods focus only on visual attention mechanism and not on keywords attention mechanism, thus leading to model-generated sentences that are not rich and accurate enough, and errors in visual feature extraction can directly lead to generated caption sentences that are incorrect. To fill this gap, we propose a combination attention module. This module comprises a visual attention module and a keyword attention module. The visual attention module helps in performing fast extractions of key local features, and the keyword attention module focuses on keywords that may appear in generated sentences. The results generated by the two modules can be corrected for each other. We embed the combination attention module into the framework of the Transformer, thus constructing a new image caption model CAT (Combination Attention Transformer) to generate more accurate and rich image caption sentences. Extensive experiments on the MSCOCO dataset demonstrate the effectiveness and superiority of our method over many state-of-the-art methods.

Link prediction, as an important research direction in complicated network analysis, has broad application prospects. However, traditional link prediction algorithms are generally designed by the sparse expression of the adjacency matrix, which is computationally expensive and inefficient, being also unable to run on large-scale networks and to preserve their higher order structural features. To fill this gap, we propose a GAN (generative adversarial network)-based link prediction algorithm. The algorithm layers the network graph, preserving the local features and higher-level structural features of the original network graph, and uses a generative adversarial model to recursively and backwardly obtain the low-dimensional vector form of the vertices in each layer of the network graph as the initialization of the network graph in the previous layer. It then obtains the low-dimensional vector form of all the vertices in the original network graph for link prediction, and the problem of local minima that can be generated by random initialization is solved. The experimental results show that our method is superior to many state-of-the-art algorithms.