## 5 Conclusions  
High-resolution optical data can observe the changes in coastal wetlands in detail. Image features can be extracted from these high-resolution data, but the impact of different features on change identification is not clear, and the combination of a large number of features could easily cause a \"dimension disaster\" problem. Therefore, the feature  
Figure 4: Wetland classification results: (**a**) 2013; (**b**) 2018; (**c**) wetland classes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**2013** & **Sea** & **Aquaculture Pond** & **Salt Marsh** & **Farmland** & **Open Water** & **Building** & **Total** \\ \hline Sea & 199.52 & 10.58 & 2.27 & 0.02 & 1.34 & 0.06 & 213.79 \\ Aquaculture pond & 0.23 & 40.5 & 0.19 & 1.08 & 0.59 & 0.22 & 42.82 \\ Salt marsh & 0.35 & 5.24 & 50.61 & 10.27 & 1.51 & 0.5 & 68.49 \\ Farmland & 0.09 & 0.38 & 0.62 & 40.66 & 0.52 & 0.88 & 43.14 \\ Open water & 0.07 & 0.93 & 0.03 & 0.04 & 10.39 & 0.03 & 11.49 \\ Building & 0.2 & 1.34 & 0.2 & 0.36 & 0.2 & 17.96 & 20.26 \\ \hline Total & 200.46 & 58.98 & 53.91 & 52.44 & 14.56 & 19.65 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Change transfer matrix between different wetland categories from 2013 to 2018 (units: km\({}^{2}\)). hierarchical selection method was proposed taking into account the jumping degree of different image features. The influence of different features on wetland classification was analyzed and those features which had a slight influence on wetland classification were deleted to not only reduce dimensionally but also to obtain optimal features subsets. In addition, only small amounts of training samples were accessible at pre- or post-changed time. Therefore, the training samples transfer learning strategy was designed for wetland classification, and the classification results at pre- and post-changed times were compared to obtain a change transfer matrix. The main conclusions are as follows:  
1. The jumping degree was introduced to design a feature hierarchical strategy in order to obtain optimal feature subsets. The feature selection results showed that the feature hierarchical selection method could provide a quantitative reference for optimal feature subsets selection. 2. The training samples transfer learning strategy was used to classify post-changed optical data without recollecting training samples. It could obviously save the effort of collecting training samples. The overall accuracy of the transferred training samples was 91.16%, demonstrating that it could ensure the accuracy requirements for change monitoring. 3. The southeastern coastal wetlands located in Jiangsu Province were used as a study area and ZY-3 images in 2013 and 2018 were used to conduct experiments. The results demonstrated that salt marshes increased mainly from the sea area (2.27 km\({}^{2}\)) because salt marshes expand rapidly throughout coastal areas and aquaculture ponds increased from the sea area (10.58 km\({}^{2}\)) and salt marshes (5.24 km\({}^{2}\)) because of the considerable economic benefits of the aquacultural industry. Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON]; investigation, [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and J.W. ; visualization, [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.