## 2 Datasets and Methods
### Methods
#### 2.3.2 OHS Preprocessing  
The procedure of OHS data preprocessing with the hyperspectral image processing software PIE-Hyp(r) 6.0 and ENVI(r) 5.6 is shown in Figure 3. There are 32 bands in the original OHS hyperspectral data [52]. First, all the bands were tested to identify any bad bands. Bands with no data or poor quality were marked as bad. If there was a bad band, it needed to be repaired. Radiation calibration [57] and atmospheric correction [58] were then carried out for the above bands, respectively.  
Hyperspectral images have rich spectral features, which can be combined with their derived features to carry out fine wetland classification. As shown in Figure 6, spectral values of different wetland types in OHS hyperspectral images were plotted according to the region of interest (ROI) of the training samples. The spectral curves of seven wetland types are relatively low, with the highest spectral reflectance of farmland and tidal flat and the lowest spectral reflectance of saltwater. The spectral reflectance curves of saltwater and river are similar with an absorption peak in the near-infrared band, but the spectral reflectance of the river is slightly higher than that of saltwater on the whole. Additionally, the spectral reflectance curves of shrub and grass are also similar, but the overall reflectance of grass is higher than that of the shrub. There is no obvious difference in spectral reflectance between _Sueda salsa_ and grass, especially in the near-infrared band, resulting in a low separability between the two types of wetlands. In conclusion, the spectral reflectance separability of the seven wetland types is not very significant, which would lead to classification errors of some wetlands and affect the accuracy of classification results to a certain extent.  
Figure 5: False color image of GF-3 texture features in the YRD (red = mean; green = variance; blue = homogeneity).  
Previous studies have shown that the Hughes phenomenon exists in the classification process due to a large number of hyperspectral bands [59]. Feature extraction, also known as dimensionality reduction, can not only compress the amount of data, but also improve the separability between different categories of features to obtain the optimal features, which is conducive to accurate and rapid classification [60].  
The classification of remote sensing images is mainly based on the spectral feature of pixels and their derived features. In this study, principal component analysis (PCA) was used as the spectral feature extraction algorithm to obtain the first five bands, whose eigenvalues were much larger than those of other bands [61]. As one of the most widely used data dimension reduction algorithms, PCA is defined as an optimal orthogonal linear transformation with minimum mean square error established on statistical characteristics [24]. By transforming the data into a new coordinate system, the greatest variance by some scalar projection of the data comes to lie on the first coordinate, which is called the first principal component, the second greatest variance on the second coordinate, etc. In addition to spectral features, we also employed normalized difference vegetation index (NDVI) [62] and normalized difference water index (NDWI) [63] to obtain index features. The formulas of NDVI and NDWI are as follows.  
\[NDVI=\frac{(NIR-Red)}{(NIR+Red)}=\frac{(B_{24}-B_{14})}{(B_{24}+B_{14})} \tag{13}\]  
\[NDWI=\frac{(Green-NIR)}{(Green+NIR)}=\frac{(B_{7}-B_{23})}{(B_{7}+B_{23})} \tag{14}\]  
where _NIR_, _Rad_, and _Green_ represent the near-infrared band, red band, and green band, respectively.  
As shown in Table 3, band 24 and band 14 from the OHS data are selected for NDVI, whereas band 7 and band 23 are suitable for NDWI. Figure 7 presents the two kinds of OHS hyperspectral index features. Both the NDVI value for land and NDWI value for water are positive, which can basically represent the spatial distribution of land vegetation and water.  
Figure 6: Spectral curves of the wetland types in the YRD derived from the OHS image.