# Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
## 4 Experimental Results and Analysis
### Zhangjiangkou Mangrove Nature Reserve HJIA-HSI Dataset  
The Environment and Disaster Detection Small Satellite (HJ) is a new civilian satellite system in China. It was successfully launched in Taiyuan at 11:25 a.m. on 6 September 2008. The HJ1A satellite is equipped with a CCD camera and hyperspectral imaging (HSI). The remote sensing data used in these experiments were derived from the HJ-HSI [20] on 28 March 2010, which involved sampling of the Zhangjiangkou Mangrove Nature Reserve in Fujian province. The mangrove area is one of the national key nature reserves. The location is \(22^{\circ}53^{\prime}45^{\prime\prime}\)\(\sim 23^{\circ}56^{\prime}00^{\prime\prime}\) N; and \(117^{\circ}24^{\prime}07^{\prime\prime}\)\(\sim 117^{\circ}30^{\prime}00^{\prime\prime}\) E, with total area of 2360 hectares. The spatial resolution of this image was 100 m and the band range were 450-950 nm, with a total of 115 bands. According to field investigations in the study area, the land  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Samples**} & \multicolumn{3}{c}{**DR+1-NN Classifier**} \\ \cline{2-7} \# & **Train** & **Test** & **SPP** & **PCA** & **SDE** & **SSEPP** \\ \hline C1 & 5 & 41 & 14.63 & 68.29 & 70.73 & 65.85 \\ C2 & 143 & 1285 & 43.97 & 45.21 & 62.02 & 68.72 \\ C3 & 83 & 747 & 38.29 & 45.92 & 44.44 & 50.87 \\ C4 & 24 & 213  
cover types are defined as being in seven categories. Table 3 lists the seven land cover types and their descriptions. In this experiment, 100 samples were randomly selected as training samples, of which 10 were mangroves and 15 were for each of the other six land cover types. Different dimensionality reduction algorithms uniformly reduced 115 spectral bands to 10 dimensions. The spectral bands had four texture features (mean, variance, dissimilarity, and second moment) [21], Digital elevation model (DEM) and normalized difference vegetation index (NDVI) [22] were integrated into one decision-making feature and a total of 16 dimensional features. Finally, the classification was performed by the 1-NN classifier. The false color composite of the Zhangjiangkou Mangrove Nature Reserve HJ1A-HSI is shown in Figure 2(a). Figure 2(b)-e shows a comparison of the classification results of different algorithms, Figure 2(b) shows the sparsity preserving projection (SPP) algorithm, Figure 2(c) shows the principal component analysis (PCA) algorithm, Figure 2(d) shows the sparse discriminant embedding (SDE) algorithm, and Figure 2(e) shows the supervised sparse embedding preserving projection (SSEPP) algorithm. A confusion matrix is a very effective way to assess classification accuracy. We used a confusion matrix, the overall accuracy, and the kappa coefficient to evaluate the classification performance of the algorithms. Using field sampling and high-resolution imagery, we randomly took 30 mangrove samples and 50 samples of the remaining six land cover types as the ground truth samples to build the confusion matrices. The confusion matrices for the classification results of the four algorithms are listed in Table 4. Overall accuracy and kappa coefficients are shown in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Name** & **Land Cover Class** & **Description** \\ \hline C1 & Mangroves & Mangrove forests \\ C2 & Upland vegetation & Deciduous or evergreen forest land, orchards, and tree groves \\ C3 & Urban areas & Residential, commercial, industrial, and other developed land \\ C4 & Water & Permanent open water, lakes reservoirs, bays, and estuaries \\ C5 & Litttoral zone & Land in the intertidal zone or the transitional zone \\ C6 & Fallow land & Fields no longer under cultivation \\ C7 & Agricultural land & Crop fields, paddy fields, and grasslands \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of land cover types. Figure 2: Overall accuracy curves of different DR algorithms in different dimensions.