# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 2 Related Works  
In recent years, generative adversarial networks (GANs) and diffusion models have been continuously applied in the field of image generation. Text-to-remote-sensing image generation methods based on diffusion models have also played a significant role in remote sensing image processing. The earliest application of GANs in remote sensing image processing was proposed by [PERSON] et al. [18], who introduced two GAN frameworks: spectral classifier and spectral-spatial classifier for hyperspectral remote sensing image classification tasks. To address the issue of low resolution in generated remote sensing images, [PERSON] et al. [19] proposed a conditional GAN for remote sensing image super-resolution, which guides the generator to produce corresponding high-resolution images based on input low-resolution images. To generate images with details that better match textual descriptions, attnGAN [20] introduces an attention mechanism that aligns the focus points of text descriptions with the corresponding regions of generated images and uses local attention at different generation stages. Later, attnGAN was improved by incorporating a multi-stage generation strategy to incrementally introduce image detail features and structurally process text descriptions to capture fine-grained textual information. Despiteimprovements in the quality of generated remote sensing images, issues such as blurriness, distortion, or deformation in certain areas still persist.  
Subsequently, researchers have progressively applied diffusion models to the field of remote sensing image generation, often combining semantic segmentation networks like U-Net with attention mechanisms to achieve the diffusion process. The U-Net network extracts image features, while the attention mechanism computes the weights of the input conditional information to establish the matching relationship between images and their text. Among these, [PERSON] et al. [21] proposed the Diffusionsat model, which is an improved version of the stable diffusion model that enables remote sensing image generation guided by metadata and text. [PERSON] et al. [22] introduced ConcededNet based on improved diffusion models, which allows multi-condition inputs to control remote sensing image generation. Its adaptive feature extraction module dynamically adjusts the generation strategy according to different input conditions, further enhancing image quality and diversity.  
Existing image generation models that commonly use convolutional neural networks (CNNs) are limited by the size of the convolutional kernels, hindering the extraction and learning of detailed image features. In contrast, the Transformer architecture, initially designed for natural language processing, can learn image features on a pixel-by-pixel basis. It also employs positional encoding to mark positions in the unfolded one-dimensional sequence of images, facilitating the generation of images rich in details. [PERSON] et al. [23] combined the Transformer architecture with energy models by leveraging two pre-trained models, VQVAE [24] and VQGAN [25]. They proposed a Hopfield network to maintain and update states during image generation, enhancing consistency between the generated images and textual descriptions, thereby resulting in remote sensing images with abundant details. The VQ-Diffusion model integrates the principles of diffusion and the Transformer architecture, demonstrating outstanding performance in general image generation. Therefore, this study uses VQ-Diffusion as the benchmark model for remote sensing image generation. To address issues such as unclear details, unreasonable spatial structures, and regional distortions in generated images, the model's structure has been improved, significantly enhancing the quality of the generated remote sensing images.