**5. Conclusions**

This study presents a new multidimensional, densely connected, convolutional network for water identification from high spatial resolution multispectral remote sensing images. It uses DenseNet as the feature extraction network to carry out image downs-sampling, then uses trans-convolution for image upsampling. On this basis, multiscale fusion is added to fuse features of di fferent scales in the down-sampling process into the upsampling process. Compared with the traditionally used water index method, the deep convolutional neural network does not need to find the index threshold, leading to reduced errors, and thus higher accuracy. Meantime, comparing the proposed DenseNet with other networks of ResNet, VGG, SegNet and DeepLab v3+, this DenseNet method requires less training time and has the fastest convergence speed besides DeepLab v3+. The overall performance of DenseNet is still much better. We also added a 95% confidence interval to the evaluation results to reduce the uncertainty caused by the limited samples. The results from the GF-1 images show that, even though DenseNet cannot identify all of the water areas, but it can identify water with grea<sup>t</sup> precision, and has much better performance in identifying the boundary between land and water, and can better distinguish the mountain shadows, towns and bare land. Its performance is also better in terms of distinguishing the cloud. Furthermore, the proposed deep learning approach can be easily generalized to an automatic program.

**Author Contributions:** Conceptualization, G.W. and M.W.; methodology, M.W.; software, M.W.; validation, G.W., M.W. and X.W.; formal analysis, G.W.; investigation, M.W.; resources, G.W.; data curation, M.W.; writing—original draft preparation, M.W.; writing—review and editing, G.W., M.W., X.W. and H.S.; visualization, G.W.; supervision, G.W.; project administration, G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Key Research and Development Program of China, gran<sup>t</sup> number of 2017YFA0603701; the National Natural Science Foundation of China, gran<sup>t</sup> number of 41875094 and 61872189; the Sino-German Cooperation Group Project, gran<sup>t</sup> number of GZ1447; the Natural Science Foundation of Jiangsu Province of China, gran<sup>t</sup> number of BK20191397 and the APC was founded by Ministry of Science and Technology of China.

**Acknowledgments:** This work was supported by National Key Research and Development Program of China (2017YFA0603701), the National Natural Science Foundation of China (41875094, 61872189), the Sino-German Cooperation Group Project (GZ1447), and the Natural Science Foundation of Jiangsu Province under Grant nos. BK20191397. All authors are grateful to anonymous reviewers and editors for their constructive comments on earlier versions of the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
