## 4 Results and Analysis
### Vegetation Classification and Accuracy Verification  
Hyperspectral image data are characterized by narrowing wavelength bands, high feature dimensions, a high spectral resolution, etc., and they can better reveal the details and features of the spectrum, making hyperspectral images advantageous in the selection of spectral indices. In order to select the optimal classification sample data, this study devised three experimental schemes: spectral features + water index + vegetation index (Scheme 1), spectral features + red edge index (Scheme 2), and spectral features + water index + vegetation index + red edge index (Scheme 3). It is important to note that the OHS hyperspectral image incorporates seven red edge bands. Following the elimination of the maximum and minimum values, five bands within the wavelength range of 686-746 nm are employed as the operational bands for the red edge index. The characterizations are detailed in Table 4.  
Referring to the field sampling coordinates, three experimental schemes were employed to select the classification samples. Utilizing the JM distance, the separability of category samples selected by different schemes was analyzed. After multiple trials, it was observed that the separability of the _Suada glauc_ vegetation and inland mudflats was comparatively low. Conversely, the separation of other category samples was satisfactory. As a result, we ranked the separation between _Suada glauc_ vegetation and inland mudflats, from high to low. Consequently, we determined that the highest separation of sample categories was selected by the combination of band + NDVI + NDWI + NDVIred3 in Scheme 3 (Table 5). The images were categorized using random forest classification, the support vector machine method, and maximum likelihood estimation to obtain the classification results of the five typical vegetation types in the Yellow River Delta in 2020 and 2022 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic Variable** & **Short Title** & **Description of Features** \\ \hline Spectral features & Band & B2–B17, B23–B25 \\ Vegetation index & NDVI & (B27–B13)/(B27 + B13) \\ Water index & NDWI & (B27–B13)/(B27 + B13) \\  & NDVIred1 & (B27–B15)/(B27 + B15) \\  & NDVIred2 & (B27–B16)/(B27 + B16) \\ Red edge index & NDVIred3 & (B27–B17)/(B27 + B17) \\  & NDVIred4 & (B27–B18)/(B27 + B18) \\  & NDVIred5 & (B27–B19)/(B27 + B19) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Phragmites** & **Suaeda** & **Tamarisk** & **Typha** & **Spartina** \\  & **Glauca** & & **Presl** & **Aterniflora** \\ \hline Phragmites & & 466–709 & 466–730 & 466–700, 806–833 & 490–700, 806–833 \\ Suaeda glauc & 443–686, 746–940 & & 510–560, 620–709 & 560–730 & 443–531, 580–746, 820–850, 896–940 \\ Tamarisk & 490–686, 746–940 & 560–709 & & 443–550, 580–730 & 580–730, 806–833 \\ Typha orientalis & 490–686, 760–896 & 560–709, 760–926 & 760–940 & 443–580, 620–746, 820–850, 896–926 \\ Spartina & 746–940 & 510–896 & 510–896, 746–896 & 746–926 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Divisibility lookup table (Unit: nm). (Figure 5). In the study area, eight distinct land cover types were identified: clear water, turbid water, inland mudflat, _Suaeda glauca_, _Tamarisk_, _Spartina alterniflora_, _Typha orientalis Presl_, and _Phragmites_. According to the classification results, 1200 sampling points were randomly generated in the ENVI software as test samples, and the samples were verified through visual interpretation by combining the data of field sampling points and remote sensing images with a 0.5 m high spatial resolution at the same time.