## 1 Introduction  
With the continuous development of earth observation technologies, increasing numbers of remote sensing satellites have been launched into orbit, which enabled the collection of a myriad of high-resolution remote sensing (HRRS) image data. High-resolution remote sensing images not only exhibit characteristics of rich spectral features, various shapes, and abundant texture features, but also contain significantly clear semantic scene information. Therefore, the classification of remote sensing image scenes is a significant approach for analyzing remote sensing image information, which has attracted considerable research efforts as a key technology for interpreting images and understanding the real world. The classification of high-resolution remote sensing scene data is the basis of several major applications of remote sensing, such as land resource management, natural resource management, and urban planning. Generally, remote sensing scene images (RSSI) reflect widely varying types of objects (including landforms in distinct conditions and buildings with diverse shapes) with complicated features. Hence, the classification of such information remains challenging. The classification of remote sensing images is hugely dependent on data labeling, in which humans input meaningful labeled data into the training model, and subsequently, such data is further used in feature modeling. However, at present, in the context of expanding applications of such data, the lack of existing pre-labeled remote sensing data becomes a significant obstacle to remote sensing scene classification research, requiring the expenditure of considerable labor and time. Thus, automated generation of marked remote sensing scene data has become practical. At present, existing methods of data generation have been mainly developed based on generative adversarial networks (GANs). Problems such as distortion and long training times have been reported in the generation of remote sensing scene data using traditional GAN networks and associated variants. [PERSON] et al. [1] proposed a gradually developed generator and discriminator GAN to generate natural images such as human faces and animals. However, the size of the generated images was limited. Subsequently, it was found that this method could not be applied to RSSIs. [PERSON] et al. [2] used pre-trained GAN models to generate natural images under conditions of extremely limited data. This is consistent with the lack of scene data addressed in the present work. However, after experiments, it was found that problems such as distortion persisted. [PERSON] et al. [3] proposed a high-resolution remote sensing scene data generation method based on a Wasserstein GAN network, which solved the problem of distortion to a certain extent. However, the size of the generated data was limited. [PERSON] et al. [4] proposed a diversified improved GAN with the ability to generate RSSIs. However, due to limitations in computing power and training time, this method nonetheless was unable to learn to generate scene images of sufficient size. Although generating RSSIs directly is evidently difficult for GAN networks, they perform effectively with respect to texture synthesis tasks. At present, several texture analysis methods have been developed. Among the statistical analysis methods, [PERSON] [5] proposed and applied an approach to describe texture features through autocorrelation function. [PERSON] et al. [6] proposed a gray difference histogram statistical method, which was able to describe the spatial organization information of grayscale images. Methods based on the characteristics of texture primitives and their arrangement rules are known as structural analysis methods. For example, [PERSON] and [PERSON] [7] used this structural analysis method and put forward texture segmentation depending on the Voronoi polygon. Texture analysis based on models assumes that the texture is distributed according to a given model. Spectrum analysis methods are constructed on the multi-scale analysis of texture.