**5. Conclusions**

Urban land use classification using VHSR remotely sensed imagery remains a challenging task due to the extreme difficulties in differentiating complex and confusing land use categories. This paper proposed a novel OUDN algorithm for the mapping of urban land use information from VHSR imagery, and the information of urban land use and urban forest resources was extracted accurately. The results showed that the OA of the UDN algorithm for urban land use classification was substantially higher than those of the U and D algorithms in terms of Spe, Spe-Index, and Spe-Texture. Object-based image analysis (OBIA) can address the problem of the "salt-and-pepper" effect encountered in VHSR image classification to a certain extent. Therefore, the OA of urban land use classification and the urban forest extraction accuracy were improved significantly based on the UDN algorithm combined with object-based multiresolution segmentation constraints, which indicated that the OUDN algorithm offered dramatic advantages in the extraction of urban land use information from VHSR imagery. The OA of spectral features combined with texture features (Spe-Texture) in the extraction of urban land use information was as high as 93.8% with the OUDN algorithm, and different land use classes were identified accurately. Especially for urban forests, the OUDN algorithm achieved the highest classification accuracy of 99.7%. Thus, this study provided a reference for the feature setting of urban

**Figure 13.** Overall classification accuracies (OA) of different features based on different algorithms.

forest information extraction from VHSR imagery. However, for the OUDN algorithms, the ratios of Agricultural Land and Grassland misclassified as Forest were higher based on Spe-Texture, which led to confusion between urban forests and other categories. This issue will be further studied in future research.

**Author Contributions:** Conceptualization, H.D.; data curation, D.Z., M.Z., Z.H., H.L., and X.L. (Xin Luo); formal analysis, S.H., X.L. (Xuejian Li), F.M., and H.L.; investigation, S.H., D.Z., Y.X., M.Z., Z.H., and X.L. (Xin Luo); methodology, S.H.; software, S.H. and Z.H.; supervision, H.D. and F.M.; validation, G.Z.; visualization, X.L. (Xuejian Li) and Y.X.; writing—original draft, S.H.; writing—review and editing, H.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation (No. U1809208, 31670644, and 31901310), the State Key Laboratory of Subtropical Silviculture (No. ZY20180201), and the Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization (No. S2017011).

**Acknowledgments:** The authors gratefully acknowledge the supports of various foundations. The authors are grateful to the editor and anonymous reviewers whose comments have contributed to improving the quality.

**Conflicts of Interest:** The authors declare that they have no competing interests.
