# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 5 Experimental Results and Analysis
false alarm or missing alarm areas, which need further analysis and judgment. Therefore, we will further compare the indicators with other methods. We used statistical accuracy (_PCC_) and Kappa index (_KC_) as experimental indicators to evaluate the performance of the algorithm [12]. If _FP_ is the number of the pixels belonging to the unchanged class but falsely classified as the changed class, and _FN_ is the number of the pixels belonging to the changed class but falsely classified as the unchanged class. _TP_ and _TN_ represent the number of changed pixels and unchanged pixels that are correctly detected, respectively. Then the correct classification accuracy (_PCC_) is:  
\[PCC=\frac{TP+TN}{N} \tag{18}\]  
where \(N\) is the total number of pixels in the image. And the Kappa index _KC_ is calculated as:  
\[KC=\frac{PCC-PRE}{1-PRE} \tag{19}\]  
where \(PRE=\frac{(TP+FP)\times(TP+FN)+(FN+TN)\times(FP+TN)}{N^{2}}\). In the experimental comparison, besides NSCTKFCM and NSCTFCM, the L-V-NSCT method was also compared with other methods in recent years, namely PCANet [11], DNN [12], and MRFFCM [13]. The experimental results are shown in Table 1.  
From the data analysis in Table 1, we can see that both the _PCC_ and _KC_ of L-V-NSCT were higher than the other five approaches. Although the _FN_ of L-V-NSCT was higher than NSCTFCM, MRFFCM and PCANet, the _FP_ was comparatively lower. So, the final _PCC_ and _KC_ of L-V-NSCT were also better than that of the 3 algorithms. We also conducted experiments with 20 of our remote sensing image databases, with the average accuracy and Kappa index obtained are shown in Figure 2.0 As can be seen from the table, among all the six methods, NSCTKFCM performed the worst and the experimental results were unstable. The experimental results of PCANet, DNN and L-V-NSCT were better. DNN, in particular, performed very well due to the use of deep neural networks. The performance of L-V-NSCT was relatively stable. Although _KC_ of L-V-NSCT was slightly lower than that of DNN, the _PCC_ was still higher than that of DNN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & _PCC_ (\%) & _FN_ & _FP_ & _KC_ \\ \hline NSCTKFCM & 95.24 & 2085 & 2749 & 0.8241 \\ NSCTFCM & 97.51 & 236 & 2286 & 0.9113 \\ PCANet & 98.22 & 944 & 863 & 0.9300 \\ DNN & 98.09 & 1059 & 883 & 0.9278 \\ MRFFCM & 97.69 & 712 & 1636 & 0.9151 \\ L-V-NSCT & 98.36 & 948 & 681 & 0.9379 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Change detection results under evaluation by different algorithms. Figure 5: Detection results with details. This method can be used to detect and analyze changes in the water environment and provide help for water environment management. We examined the changes in the Dongting Lake in China. The remote sensing images of Dongting Lake from January 1973 and February 2014 are shown in Figure 6a,b. Figure 6a was derived from landsat1 with a resolution of 79 m, while Figure 6b was derived from landsat8 with a resolution of 15 m.  
Images collected in the same season can better reflect the real changes in the lake. Figure 6c shows the changes in Dongting Lake on a large scale. The region with the greatest change in the map showed that, after 40 years, the area of some local watersheds had changed, and some local watersheds have changed their original route. Although the results of large-scale detection do not provide specific details, the results can provide location reference for further research. Upon further analysis, changes in the rainy season and dry season in Dongting Lake are shown in Figure 7. The original image size is 4400 \(\times\) 4000, and the resolution is 10 kilometers [20]. Because of the cloud interference in Figure 7b, the detection result is affected. After removing the cloud and text interference information by color detection [21], the detection result is shown in Figure 7d, and the number of pixels is 8786. The results of change area detection provide a basis for water storage, flood prevention and landform change analysis of Dongting Lake.