# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 1 Introduction  
Remote sensing image generation holds significant importance in fields such as Earth observation, resource management, and environmental protection. When the cost of obtaining actual data is high or there is a lack of data, generating high-quality remote sensing images can provide essential support for modeling and analysis. Such image generation can not only enhance the application of few-shot learning in remote sensing but also expand the coverage of remote sensing tasks, including disaster monitoring, land use analysis, and ecosystem change tracking. With the rapid development of generative models such as generative adversarial networks (GANs) and diffusion models, remote sensing image generation technology has made significant progress in improving generation accuracy and efficiency. However, current technologies still face challenges such as the realism of generated images, resolution enhancement, and semantic accuracy. Therefore, there is a need for improved remote sensing image generation techniques to provide high-qualityimage data for remote sensing image processing tasks. Currently, there are three main architectural approaches to image generation methods: autoencoders, generative adversarial networks (GANs), and diffusion models. Autoencoder neural networks, introduced by [PERSON] et al. [1], are unsupervised learning algorithms that can be used for high-dimensional complex data processing. In 2010, researchers [2] applied autoencoders to image reconstruction and feature learning, marking the first use of autoencoder networks for image generation. [PERSON] et al. [3] proposed the variational autoencoder (VAE), a probabilistic generative model, which achieved significant progress in image generation and latent space interpolation. VAE has since become a popular method in image generation models. However, due to the limitations of the latent space and the insufficient handling of image space consistency, the realism and detail of images generated using autoencoder-based models often fall short compared to other generative models. Therefore, traditional autoencoders usually need to be combined with other generative models to better meet the needs of remote sensing image generation. For instance, [PERSON] et al. [4] introduced boundary equilibrium GANs (BEGANs), which balance image quality and diversity using both autoencoder loss and generative adversarial network loss. Since their introduction by [PERSON] et al. [5] in 2014, generative adversarial networks (GANs) have been widely applied in image generation. The principle behind GANs involves two adversarial neural networks (a generator and a discriminator) that compete with each other. This competition drives the generator to continuously improve its generated images to the point where the discriminator has difficulty distinguishing between the real and generated images, thereby enhancing the quality of the generated images. However, early GANs lacked controllability in their generated images, and the training process was unstable. Although conditional generative adversarial networks (cGANs) [6] introduced conditional generation for specific types of images, thereby improving controllability, they still struggled to capture image details when generating complex types of objects, often resulting in blurriness and distortion. The introduction of diffusion models has provided new solutions in the field of image generation. The concept of diffusion models, proposed by [PERSON] et al. [7], is inspired by non-equilibrium statistical physics. The core idea involves systematically disrupting the data distribution structure through an iterative forward diffusion process and then learning the reverse diffusion process to restore the data structure, resulting in a highly flexible and manageable generative model. [PERSON] et al. [8] advanced this concept with the denoising diffusion probabilistic model (DDPM), which learns the data distribution through a progressive noise injection process and generates new samples through a reverse denoising process.