## 1 Introduction  
As an important part of the cryosphere, permafrost is mainly distributed in the high-latitude and high-altitude area of the northern hemisphere, and the areal extent of permafrost regions (permafrost probability \(>\) 0) is approximately 19.82 \(\times\) 10\({}^{6}\) km\({}^{2}\)[1]. Permafrost research is the basis for studying the surface hydrological process, climate change, and ecological environment change. It is of great significance for understanding the soil water/ice condition, geomorphic process, and geomorphic and landscape development, as well as the stability of engineering construction and disaster reduction. In the 21 st century, the warming rate of the pan-Arctic permafrost region is expected to be twice the global average warming rate [2]. Accelerated warming leads to the degradation of permafrost, including the increase in the permafrost temperature, the deepening of active layer thickness (ALT), and the reduction in the permafrost area [3, 4]. Permafrost degradation not only aggravates climate and ecological changes but also affects biochemical processes and human infrastructure [5]. Therefore, under the background of climate warming, high-precision permafrost mapping is of great significance for assessing regional ecology, hydrology, and especially engineering impact [6; 7]. The response of the permafrost region to climate change has obvious spatial heterogeneity. At the local scale, there are many factors affecting the distribution of permafrost, including geographical location, terrain, climate, vegetation, snow cover, water body, and soil type. These factors regulate the regional energy exchange process, and therefore also regulate the temperature of the ground and soil [8]. The distribution of permafrost blocks, vegetation, and carbon sequestration in forests and wetlands are all significantly influenced by topography, which also regulates the redistribution of soil water in both horizontal and vertical directions [9; 10]. The annual and seasonal changes in air temperature directly affect the thawing rate and maximum thawing depth of the active layer (AL). The physical and mechanical properties of permafrost have obvious temperature dependence [11]; precipitation directly affects ALT by changing the hydrothermal properties of the AL [12]; snow affects the distribution of permafrost and its response to climate change through its albedo, emissivity, absorptivity, and latent heat and low thermal conductivity [13]; vegetation affects the distribution of permafrost by intercepting snow, reducing solar radiation and changing the water and thermal conditions of the AL [14]; soil type also has an important impact on the degradation of permafrost [3]. The current monitoring methods of permafrost use surface indicators, such as surface temperature, vegetation cover, topography, and geomorphology, or the combination of different surface characteristics [15] to qualitatively infer the occurrence conditions of permafrost. Permafrost exists below the surface with uneven properties, and most of them are located in remote areas. Therefore, it is difficult to obtain field measurements of permafrost conditions. With the development of permafrost-monitoring tools and remote sensing technology, the availability of high-resolution remote sensing data provides strong data support for the study of permafrost at the micro-scale; statistical and machine learning classification algorithms have also been successfully applied to the inversion of permafrost spatial distribution [16]. Texture classification is a computer vision task aimed at categorizing images or image regions based on their texture features. This task relies on extracting texture features, such as gray-level co-occurrence matrix (GLCM) [17] and local binary patterns (LBP) [18], to describe the statistical and local texture structures within the image regions.