**4. Discussion**

The UDN and OUDN algorithms constructed by this study achieved higher accuracies in the extraction of urban land use information from VHSR imagery than the U and D algorithms. The UDN algorithm applied the coupling of the improved 11-layer U-net network and the improved DenseNet to train the network and realize prediction using the learned deep level features. With the advantages of both networks, the accurate extraction of urban land use and urban forests was ensured. Meanwhile, the UDN algorithm addressed the problems of common misclassifications and omissions in the classification process (Tables 4–6) and dealt with the confusion of Agricultural Land, Grassland, Barren Land and Built-up (Figure 12), thereby improving the classification accuracies of urban land use and urban forest. In all feature combinations, especially for the Spe-Texture, the classification accuracies of the UDN algorithm were 3.4% and 3.5% higher than those of the U and D algorithms, respectively. This study chose 50 as the optimal segmentation scale, and the phenomenon of misclassification with UDN was corrected by the constraints of the segmentation objects (Tables 4–6). The OUDN algorithm not only alleviated the distribution fragmentation of ground objects and the common "salt-and-pepper" phenomenon in the classification process but also dealt with the problem of discontinuous boundaries during the splicing process of the classification results of segmented image blocks (Figures 8–10). Compared with previous studies about classifications using U-net and DenseNet [41,46], this study fully combined the advantages of U-net and DenseNet network and achieved more high classification accuracies. Compared with previous object-based DL classification methods [49], in this study, object-based multiresolution segmentations were used to constrain and optimize the UND classification results and not used to participate in the UND classification. It is necessary to further study in this respect.

The overall classification accuracies (OA) of di fferent features based on di fferent algorithms are shown in Figure 13. (1) In terms of the UDN and OUDN algorithms, accuracies of the Spe-Texture were the highest (93.2% and 93.8%), followed by those of the Spe-Index (92.3% and 92.6%). As demonstrated by Figure 12, for Grassland, Built-up and Water, the classification accuracies of the Spe-Texture were significantly higher than those of the Spe-Index. For example, the Grassland accuracies of the Spe-Texture were 8% and 10% higher than those of the Spe-Index, and the Built-up accuracies of the Spe-Texture were 2% and 2% higher than those of the Spe-Index. It can be concluded from Table 3 that the TA and VA of Spe-Texture are higher. Thus, the classification results of the Spe-Texture were better than those of the Spe-Index and Spe. (2) In terms of the U and D algorithms, classification accuracies of the Spe-Texture were the lowest, 21%, 25% of the Grassland, and 13% and 17% of the Barren Land

were misclassified as Forest and Built-up, respectively. In contrast, the accuracies of Spe-Index were the highest.

For urban forests, after texture was added to the Spe, that is, based on the Spe-Texture, the UDN and OUDN algorithms achieved the highest classification accuracies (approximately 100%) for extracting the information of urban forests from VHSR imagery. Similarly, the U and D algorithms also offered relatively obvious advantages for extracting urban forest information based on the feature. As shown in Figure 12, (1) for the U algorithm, the Spe-Index yielded the lowest urban forest extraction accuracy. However, based on Spe-Texture, the accuracy is the highest (99%), and the ratio of Grassland misclassified as Forest was lower (10%) than that of Spe (12%), so the Spe-Texture offered advantages in the extraction of urban forests. (2) For the D algorithm, the urban forest extraction accuracy with the Spe-Texture, compared with those with the Spe and Spe-Index features, was the highest; meanwhile, the ratios of Agricultural Land and Grassland misclassified as Forest were the lowest (2% and 8%). (3) For the UDN and OUDN algorithms, although the urban forest extraction accuracy based on the Spe-Texture was the highest, the ratios of Agricultural Land and Grassland that were misclassified as Forest, compared with those of the other features, were the highest (5% and 8%), thereby resulting in confusion between urban forests and other land use categories.
