# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis
### Datasets and Evaluation Metrics  
The Remote Sensing Image Caption Dataset (RSICD) [28] is a commonly used dataset in the field of multimodal learning for remote sensing. It contains 10,921 image-text pairs, covering 30 categories such as playgrounds, bridges, beaches, deserts, and cities. As a critical resource for training text-to-remote-sensing image models, it possesses significant academic value in the field of remote sensing image understanding due to its large scale, rich semantic descriptions, and multi-source image acquisition characteristics. This dataset not only provides 10,921 images along with five corresponding descriptions for each image, enhancing the model's understanding and generalization of complex remote sensing scenes, but also presents challenges for accurate classification and recognition due to its high intra-class diversity and low inter-class variability. Moreover, the RSICD supports a multi-task evaluation framework, including but not limited to image classification, retrieval, and object counting, making it a comprehensive benchmark for evaluating model performance. Cross-dataset comparative studies further expand its academic application scope, and performance improvements of fine-tuned models on this dataset, such as the significant increase in top-1 accuracy of the CLIP model [29], further demonstrate the important role of the RSICD in advancing intelligent remote sensing image analysis technologies. Therefore, this paper selects the RSICD as the training dataset for the text-to-remote-sensing image generation model, with the training, validation, and test sets divided in an 8:1:1 ratio. Figure 4: Principle of the local perception mechanism. The evaluation metrics used in this study to assess the quality of generated images are commonly used in the field of image generation: Frechet Inception Distance (FID) [30], Contrastive Language-Image Pre-training (CLIP) score, and Inception Score (IS) [31]. FID primarily measures the distribution distance between real and generated image features. First, the generated images are embedded into the latent feature space of the selected layer of the inception network, and the embeddings of the generated and real images are treated as two continuous multivariate Gaussian samples to facilitate the calculation of their means and covariances. The calculation process is detailed in Equation (11). \[FID=||\mu_{r}-\mu_{g}\mid_{2}^{2}+T_{r}(\Sigma_{r}+\Sigma_{g}-2(\Sigma_{r} \Sigma_{g})^{\frac{1}{2}}) \tag{11}\]  
where \(\mu_{r},\Sigma_{r}\) represent the mean and covariance of the feature distribution of the real images, and \(\mu_{g},\Sigma_{g}\) represent the mean and covariance of the feature distribution of the generated images. \(||\mu_{r}-\mu_{g}\mid\mid_{2}^{2}\) denotes the Euclidean distance between the means of the feature distributions of the real and generated images, and \(T_{r}\) denotes the trace of the sum of the covariance matrices, which is the sum of the diagonal elements of the square matrices. CLIP is used to evaluate the semantic alignment between generated images and their corresponding input semantic information. It measures their correlation by calculating the cosine similarity between image and text embeddings. The model converts images and texts into high-dimensional vectors, and the similarity between these vectors is calculated to obtain the CLIP score, as detailed in Equation (12). \[\text{CLIP}_{score}(I,T)=\tau\cdot\frac{f_{i}(I)}{||f_{i}(I)||}\cdot\frac{f_{t }(T)}{||f_{t}(T)||} \tag{12}\]  
where \(f_{i}(I)\) represents the feature vector extracted using the image encoder; \(f_{t}(T)\) represents the vector extracted by the text encoder, and \(||f_{t}(I)||\) and \(||f_{t}(T)||\) are the L2 norms (normalized to unit length) of the respective feature vectors. \(\tau\) is the scaling parameter used to adjust the score range. The Inception Score (IS) is a widely used metric for evaluating the performance of generative models.