## 1 Introduction  
The wetland ecosystem is often referred to as the \"kidney of the earth\", and it plays an important role in regulating the climate and maintaining biodiversity [1; 2]. The Yellow River Delta wetland is the world's most extensive, well-preserved, and youngest wetland ecosystem in the warm temperate zone. However, it faces challenges to its stability due to both environmental factors and human activities, leading to significant changes in wetland resources [3; 4]. The Yellow River Delta wetland is home to a rich variety of vegetation [5]. However, the complex and indistinct boundaries between different vegetation types in this area can lead to reduced classification accuracy on remote sensing images due to the phenomenon of \"the same object with different spectra, the same spectrum with different objects\" [6]. Accurately extracting wetland vegetation information and monitoring its temporal dynamic changes are of great practical significance for the conservation and restoration of the wetland ecology in the Yellow River Delta. Hyperspectral image data are characterized by a narrow band, high feature dimensions, and high spectral resolution, which make them well suited to the identification of spectral details and features, and they exhibit greater potential in analyzing the reflectance spectral variability of vegetation [7]. At present, some promising results have been yielded in the use of hyperspectral data for vegetation information extraction and classification. For example, [PERSON] [8] and others analyzed the spectral characteristics of five typical vegetation species based on the measured hyperspectral data of the typical vegetation species in Poyang Lake and extracted the spectral difference bands by comparing the original spectra with the differential spectra, so as to effectively classify the five species of vegetation. [PERSON] [9] and others, based on the measured hyperspectral data of five typical vegetation species in the Honghe area, compared 38 vegetation indices, demonstrating the applicability of hyperspectral vegetation indices and evaluating the classification of the five vegetation species. [PERSON] [10] et al. realized the fine classification of the Zalong wetland based on the HJ-1A hyperspectral image by comparing and analyzing the FCLS algorithm and SUFCLS algorithm. [PERSON] [11] et al., based on coupled WorldView-2 and Zhuhai-1 images, utilized the random forest algorithm to compare the effects of four different feature selection algorithms and different spatial resolutions on the classification accuracy of mangrove species. [PERSON] [12] et al. utilized Zhuhai-1 hyperspectral data to extract the research threshold range through the NDWI-NDVI index thresholding method. They compared four different supervised classification methods and achieved the high-precision extraction and analysis of mangrove forests in coastal areas of Guangdong Province. In the exploration of species identification methods for remote sensing data, more and more studies are integrating hyperspectral remote sensing with machine learning algorithms for plant classification. This integration has proven effective in enhancing the accuracy of identification and classification, leading to generally higher classification accuracy [13]. [PERSON] et al. [14] extracted 63 spectral feature variables of eight plants in agricultural regions. They employed three machine learning methods--k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF)--for the classification and identification of these plants. [PERSON] [15] conducted research on a plant classification and identification model based on KNN and RF, using the extracted feature bands in urban greening and economic crop detection. [PERSON] [16] et al. constructed a random forest classification model with 24 variables. They compared this model with other classifications of machine learning algorithms, such as SVM, KNN, and a maximum likelihood classifier (MLC), to classify the vegetation of a desert grassland.