## 1 Introduction  
Forests play crucial roles in the economy, ecology, and society, exerting positive influences on the livelihoods of local residents, promoting sustainable economic growth,and mitigating climate change. Serving as habitats for diverse flora and fauna, forests provide essential environmental services, expanding their influence from rural and local economies to encompass social functions at both local and global levels [1]. Within the framework of the \"17 + 1\" (China-Central and Eastern European Countries Cooperation), forestry cooperation carries undeniable importance. Recognizing the critical role of forests from the onset, China and Central and Eastern European countries have forged partnerships among commercial and research entities, with government support, to enhance forestry cooperation. Located along the Belt and Road Initiative, the Czech Republic (hereinafter referred to as Czech) possesses abundant forest resources. This collaborative endeavor between China and Czech promotes economic prosperity, ecological balance, and social sustainability, making substantial contributions to the sustainable advancement of forestry for both nations [2]. Hence, it is crucial to acquire the spatial distribution of forest resources in Czech. Traditional field-based forest surveys are costly, inefficient, labor-intensive, and time-consuming, posing challenges in meeting the needs for large-scale forest resource assessments. Conversely, remote sensing technology offers substantial advantages in acquiring forest resources, including extensive monitoring coverage, cost-effectiveness, and timely data availability, demonstrating significant potential [3; 4; 5]. Therefore, the utilization of remote sensing image classification methods to obtain land cover classification results can facilitate the acquisition of spatial distribution information for forest resources. Traditional remote sensing image classification methods are generally classified into two types: Pixel-based classification and object-oriented classification. In pixel-based classification methods, [PERSON] et al. [6] utilized artificial neural networks to classify Brazilian tropical forests, demonstrating superior performance compared to traditional approaches. [PERSON] et al. [7] classified forest types into four categories--broadleaf forests, coniferous forests, mixed forests, and other land uses--using dynamic clustering and combined supervised classification methods. The study concluded that the combined supervised classification method achieved higher classification accuracy. In object-oriented classification methods, [PERSON] et al. [8] conducted land cover classification in the Qianjiangyuan National Park using Sentinel-2 and Sentinel-1 data, comparing pixel-based and object-oriented approaches. They found that the object-oriented method produced superior classification results. Similarly, [PERSON] et al. [9] compared pixel-based and object-based methods for urban vegetation classification, with results indicating the superiority of object-oriented classification. The above studies demonstrate that, compared to pixel-based methods, object-oriented approaches offer advantages in classifying images by clustering individual pixels based on spectral characteristics into textured geospatial objects. This approach effectively mitigates the occurrence of \"salt and pepper\" noise in classification results and enhances better overall coherence. The key to land cover classification lies in feature extraction and classification methods. Feature selection and extraction are critical factors influencing the accuracy of land cover classification [10]. However, existing studies exhibit some limitations in feature selection. Firstly, traditional land cover classification features mainly depend on spectral information, which is sensitive to factors such as illumination and seasonal changes [11], thus constraining classification accuracy.