# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 3 Experimental Results and Discussion
### Object-Oriented Change Detection
The LETRIST, LDZP, and OMTLBP methods have similar detection results regarding anti-interference, but the proposed method is the most similar to the standard results, as shown in Figure 10a7,a8,b7,b8. Figure 10: Enlarged images of Sentinel-2 in Areas 1 and 2: (**a1,b1**) Spectral-based, (**a2,b2**) GLCM, (**a3,b3**) LETRIST, (**a4,b4**) LDZP, (**a5,b5**) OMTLBP, (**a6,b6**) GLCM-SBS, (**a7,b7**) the proposed algorithm, and (**a8,b8**) the standard change detection result. In order to verify quantitatively the effectiveness of the proposed method, the missed detection rate, false detection rate, overall accuracy rate, and Kappa coefficient are calculated, the results of which are listed in Table 3; this is accomplished by counting the number of correctly detected superpixel blocks. It can be seen that the accuracy of Comparison 1 is the lowest--the missed detection rate is 48.15%, the error detection rate is 39.13%, and the overall accuracy is only 60%. The addition of textures achieves an accuracy of greater than 90%. Among them, the error of GLCM is larger, and its false detection rate is 9.09%. The LETRIST method tends to false detection, as the false detection rate is much larger than the missing detection rate, and the results of the LDZP are just the opposite. The OMTLBP effectively improves the above issues by introducing different scales and unifies the missing and false detection rates--the OA is 97.83% and the Kappa coefficient is 0.95. The GLCM-SBS method performs better than the GLCM, but worse than the OMTLBP. The proposed method achieves the highest values due to the help of considering channel correlation--its OA is 98.15%, and the Kappa coefficient is 0.96. For the Skysat images, there are 210 samples, where the number of positive samples is 90. As in the case of the Sentinal-2 data, 70% of the total samples randomly selected are used for training. The test set includes all the samples and 50 additional superpixels. Figure 11 is the change detection results of Skysat, where Figure 11a--g illustrate Comparisons 1-6 and the proposed algorithm, and Figure 11h shows the standard change detection result, which is determined through manual visual interpretation. The white area represents the unchanged superpixels, the green area represents the changing area corresponding to the standard, the red area represents false detection, and the blue area represents missing detection. It can be seen that the missing detections are distributed in the upper right corner, and each algorithm has varying degrees of missed detections. The LBP family has difficulty describing whole characteristics because of its local texture descriptor, and there are a lot of false detections, as shown in Figure 11c-e. As can be seen, the better results are attributed to the GLCM-SBS method and the proposed algorithm. Figure 12 shows the enlarged images of the change detection results in the different areas that were indicated in Figure 11. Figure 12a--a8 illustrates the spectral-based, GLCM, LETRIST, LDZP, OMTLBP, and GLCM-SBS methods as well as the proposed algorithm and the standard of Area 1; Figure 12b--b8 show the corresponding images of Area 2. It can be seen that the false detection instances are distributed around the previous bare soil areas, and the missing detections are around the urban areas. The spectral-based, GLCM, LETRIST, and LDZP methods have a lot of missing detections and false detections, as shown in Figure 12a--a4,b1--b4. In general, among these algorithms, the proposed algorithm has higher similarity with the standard image.