# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 4 Results and Discussions
### Discussion  
Based on the three performance comparisons, we observed that the feature-based approach outperformed the tested pixel-based approach. By considering features as a unit of change, and by applying feature matching, we could successfully remove many false alarms and noisy changes. We also observed a drawback to our approach, in that it could not handle featureless objects. Feature points (red circles) were not extracted from objects at small sizes or with low contrasts, as shown Figure 13. Since our approach relies on features to decide on change or no change, featureless objects are removed from the decision. This increases omission errors, and hence, decreases precision. This is the reason our approach could not reach a recall of 1 in Figures 11 and 12.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study Area**} & \multirow{2}{*}{**Domain**} & \multicolumn{3}{c}{**Changed Object**} & \multicolumn{3}{c}{**Unchanged Object**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**False**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1**} \\ \cline{3-3} \cline{5-10}  & & **Correctly** & & & & & & & & & \\  & & **Detected** & & & & & & & & & \\ \hline \multirow{3}{*}{Incheon} & Pixel & 291 & 109 & 313 & 37 & 0.8053 & 0.8872 & 0.1057 & 0.7275 & 0.7995 \\  & AKAZE & 300 & 100 & 330 & 20 & 0.8400 & 0.9375 & 0.0571 & 0.7500 & 0.8333 \\  & SIFT & 278 & 122 & 335 & 15 & 0.8173 & 0.9488 & 0.0429 & 0.6950 & 0.8023 \\ \hline \multirow{3}{*}{Chiligok} & Pixel & 62 & 20 & 21 & 14 & 0.7094 & 0.8158 & 0.4000 & 0.7561 & 0.7848 \\  & AKAZE & 67 & 15 & 28 & 7 & 0.8120 & 0.9054 & 0.2000 & 0.8171 & 0.8590 \\  & SIFT & 64 & 18 & 26 & 9 & 0.7692 & 0.8767 & 0.2571 & 0.7805 & 0.8258 \\ \hline \multirow{3}{*}{Seoul} & Pixel & 126 & 22 & 116 & 36 & 0.8067 & 0.7778 & 0.2368 & 0.8514 & 0.8129 \\  & AKAZE & 108 & 40 & 138 & 14 & 0.8200 & 0.8852 & 0.0921 & 0.7297 & 0.8000 \\  & SIFT & 104 & 44 & 138 & 14 & 0.8067 & 0.8814 & 0.0921 & 0.7027 & 0.7820 \\ \hline \multirow{3}{*}{Mean} & Pixel & - & - & - & - & 0.7738 & 0.8269 & 0.2475 & 0.7783 & 0.7991 \\  & AKAZE & - & - & - & - & 0.8240 & 0.9094 & 0.1164 & 0.7656 & 0.8308 \\ \cline{1-1}  & SIFT & - & - & - & - & 0.7977 & 0.9023 & 0.1307 & 0.7261 & 0.8034 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of change detection results in the object domain.  
Figure 12: The ROC curves for pixel-based and feature point-based approaches with respect to the number of object changes in (**a**) Incheon, (**b**) Chiligok, and (**c**) Seoul. \(X\)-axis represents false alarm rate and \(Y\)-axis recall.  
We might address this problem by using pixel-based change detection on featureless objects. In this paper, we used pixel-based change detection simply to calculate the magnitude of feature change. Perhaps we need more intelligent schemes for merging pixel-based and feature-based change detection results. This issue, however, is left as a future research topic. We tested two representative feature extractors, AKAZE and SIFT, for feature-based change detection. We observed that both feature extractors led to the change detection performance in favor of our feature-based approach.