# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 5 Experimental Results and Analysis  
To verify the effectiveness of the L-V-NSCT method proposed in this paper, we used real datasets to conduct experiments and analyze the results. Before the change detection, we first carried out the image gray normalization, texture enhancement, and other preprocessing. The flow chart of L-V-NSCT change detection is shown in Figure 1.  
We first detected the changes of lakes and water bodies in Sardinia area [17], as shown in Figure 2. The size of the image was 412 \(\times\) 300. The comparison algorithms were NSTKFCM [18] and NSTFCM [19], both of which were improvements of the NST algorithm. Figure 2a is the image of the Sardinia region in April 1999, Figure 2b is the image of the Sardinia region in May 1999, Figure 2c is the result of the NSTKFCM method, Figure 2d is the result of the NSTFCM method, Figure 2e is the result of L-V-NSCT and Figure 2f is the reference image of the result, where the number of changed pixels was 7626. Figure 1: The flow chart of L-V-NSCT change detection. From the results, we can see that three detection algorithms achieved good results for the change detection of rivers and lakes in the image. However, the other two algorithms were greatly affected by noise, the edges of the river change areas were blurred, and the fragments change areas (areas of no concern) caused by false alarm were relatively increased. Nonetheless, the L-V-NSCT approach can effectively preserve the details of the change area and show that the edge of the main change area is clear. The above experiments showed that L-V-NSCT approach has some advantages in detecting changes in remote sensing images. In the experiment, we found that the indexes of the L-V-NSCT approach were different when different types of images were analyzed. For low-brightness images, the bit error rate of change detection increased, and the accuracy decreased. Radarsat images of Ottawa, Canada, from May 1997 and August 1997 were the original images of the dataset in the second experiments [11; 12; 13], as shown in Figure 3. The image size is 290 \(\times\) 350, and the number of changed pixels is 16049. Figure 2: Detection results in Sardinia area. (**a**) the image of the Sardinia region in April 1999, (**b**) the image of the Sardinia region in May 1999, (**c**) the result of the NSCTKFCM method, (**d**) the result of the NSCTFM method, (**e**) the result of L-V-NSCT, and (**f**) the reference image of the result. Figure 4 shows the low-frequency difference image, the high-frequency difference image, and the final detection result. Figure 4a,b are the low-frequency difference image based on the logarithmic variation function and the low-frequency difference coefficient image after further clustering, respectively. Figure 4c is the high-frequency difference coefficient image after similarity analysis. Figure 4d is the NSCT inverse transform fusion image. Figure 4e is the clustering result image, and Figure 4f is the last change detection binary image. As seen from the images, L-V-NSCT can detect the changes in the registered images very well, and the details are relatively complete. Figure 5 further reflects the detail effect of L-V-NSCT change detection. The left image is the manual labeled change detection reference image, and the right image is the detection result of this method. The labeling parts in the right figures show that the proposed method can obtain results consistent with the manual judgment in most local areas. The left parts with circles are marked with circles. Figure 4: Test results in Ottawa area. (**a**) the low-frequency difference image based on the logarithmic variation function, (**b**) the low-frequency difference coefficient image after further clustering, (**c**) the high-frequency difference coefficient image after similarity analysis, (**d**) the NSCT inverse transform fusion image, (**e**) the clustering result image, and (**f**) the last change detection binary image. Figure 3: Original image and change detection map in Ottawa area.