# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 3 Experimental Results and Discussion
### Object-Oriented Change Detection  
To verify the effectiveness of the proposed method, six comparative experiments are designed, as shown in Table 2. In order to ensure the consistency of the experimental environment, all experiments are trained and evaluated on the basis of the random forest model. For the Sentinel-2 images, there are 280 samples, where the number of positive samples is 90. According to the machine learning rules, 70% of the total samples randomly selected are used for training. The test set includes all samples and 50 additional superpixels. If the difference in the number of positive and negative samples in the training data set is too large, the training samples are redetermined until they meet the requirements. Figure 9 illustrates the change detection results of Sentinel-2, where Figure 9a-g show Comparisons 1-6 and the proposed algorithm, and Figure 9h shows the standard change detection result determined through manual visual interpretation. The white area represents 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 there are many missing and false detections when only the spectral characteristics are considered, as shown in Figure 9a. After adding the texture features extracted by the GLCM and the LBP family, the errors are significantly reduced, as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment Name** & **Basic Operating Unit** & **Feature Set** & **Feature Filtering** & **Feature Filtering Method** & **Reference** \\ \hline Comparison 1- & Object-level & Spectral features & No & - & - \\ Comparison 2- & Object-level & Spectral features and & No & - & [27] \\ GLCM & Object-level & GLCM texture features & No & - & [30] \\ Comparison 3- & Spectral features & No & - & [31] \\ LETRIST & Object-level & Spectral features and & No & - & [31] \\ Comparison 4- & LDZP & Object-level & Spectral features and & No & - & [32] \\ Comparison 5- & Object-level & OMTLBP texture features & No & - & [33] \\ Comparison 6- & Object-level & Spectral features and & Yes & \
\begin{tabular}{c} Single \\ backward \\ search \\ \end{tabular} & [27; 43] \\ The proposed algorithm & Object-level & Spectral features and & Yes &
\begin{tabular}{c} Cumulative \\ backward \\ search \\ \end{tabular} & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental scheme design. shown in Figure 9b-f. This effectively verifies the benefit of the texture characteristics in remote sensing change detection. The proposed algorithm extracts the texture characteristics on the basis of the CCM by considering the correlation among bands, and it filters the features by the CBSS, which greatly reduces the rate of missing and false detection. There are almost no red or blue regions, as shown in Figure 9g. Figure 9: Change detection results of comparisons and the proposed algorithm of Sentinel-2: (**a**) Spectral-based, (**b**) GLCM, (**c**) LETRIST, (**d**) LDZP, (**e**) OMTLBP, (**f**) GLCM-SBS, (**g**) the proposed algorithm, and (**h**) the standard change detection result. Figure 10 illustrates the enlarged images of the change detection results in the different areas that were indicated in Figure 9. Figure 10a1-a8 show the spectral-based, GLCM, LETRIST, LDZP, OMTLBP, and GLCM-SBS methods as well as the proposed algorithm and the standard of Area 1; Figure 10b1-b8 shows the corresponding images of Area 2. Through an analysis of a large number of areas, it is found that the spectral-based method can detect only the changing area that completed the change, and the spectrum is absolutely homogeneous. Among the four texture feature extraction algorithms, the traditional GLCM is inferior to the improved LBP family, and the LBP family has some missing detection on the absolute change area.