# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 1 Introduction
Subsequent research by [PERSON] et al. [9] demonstrated that diffusion models outperform generative adversarial networks in the field of image generation. This approach excels in generating image details, diversity, and realism, and it has gradually become a focus of research in remote sensing image generation. Although models like OpenAI's DALLE2 [10] and [PERSON] et al. 's stable diffusion model [11] have made significant advancements in image generation, enabling the creation of images in various styles (e.g., anime, oil painting) based on text prompts and keywords, research focused on remote sensing image generation remains relatively scarce. Existing methods for remote sensing image generation, such as the multi-stage structured generative adversarial network (StrucGAN) proposed by [PERSON] et al. [12], still face challenges like low semantic matching between generated images and textual descriptions, spatial structure errors, and poor fidelity in generated images. Consequently, remote sensing image generation encounters unique challenges, such as high resolution, multi-scale features [13], complex backgrounds, and a lack of annotated data. Specifically,tasks involving remote sensing images demand high resolution and richness in detail. Additionally, the complex and diverse backgrounds found in remote sensing images (e.g., urban areas, forests, and bodies of water) require models to accurately understand and describe these semantic details [14]. To address the aforementioned challenges, this paper employs a method combining diffusion models with the Transformer architecture [15] based on the VQ-Diffusion model [16]. The model is modified and trained to obtain the text-to-remote-sensing image generation model, RSVQ-Diffusion. Experimental results demonstrate that the modified model ensures the quality of the generated remote sensing images, producing spatially coherent images with significant improvements in diversity and realism. Moreover, it alleviates issues such as image distortion and semantic accuracy. The specific contributions of this work are as follows:  
1. The architecture combining diffusion models with the Transformer is applied to remote sensing image generation. To address the spatial characteristics of remote sensing images, spatial position encoding is incorporated into the model's image decoder, enhancing the Transformer model's ability to capture the overall spatial positional information of remote sensing images during the sequence processing;
2. The concept of local feature extraction from sequences [17] is integrated with the self-attention mechanism of the Transformer, leading to the proposed TransLocalBlock module. By controlling the sequence length and combining it with the multi-head attention mechanism, this module enables the Transformer to focus on local information during the processing of long sequences;
3. We designed ablation experiments and compared them with existing text-to-remote-sensing image generation models. Through evaluation metrics and improvements in aspects such as the spatial structure, realism, and details of the generated images, the effectiveness of the proposed method is effectively demonstrated. The organization of the paper is as follows: Section 2 describes various studies related to the generation of remote sensing images from text. Section 3 provides a detailed description of the proposed model's improvement methods. Section 4 outlines the datasets and evaluation metrics used in the experiments, followed by a comprehensive presentation and analysis of the experimental results. Finally, Section 5 summarizes the proposed method and offers an outlook for future work.