| dc.description.abstract | In the particular situation, we might get noise from the high reflectance region e.g. Building area, and it exactly affect our classification result and accuracy in vegetation regions. Thus, the purpose of this research is to detect green space coverage in urban areas, that is, to identify Vegetation and non-Vegetation area. However, the pixels of vegetation near these areas that mentioned above will be disturbed to some extent, which will cause problems in manual interpretation. Therefore, we use the Sentinel-2 image as a reference, selecting the pixels as the ground truth data manually. The training samples and test samples were selected from the target image after radiation correction base on the ground truth. The research area of this experiment is the ＂Zhunan Toufen Urban Planning Area＂, the clear and cloudless images of this area are selected, which can reduce the error and improve the accuracy during training and testing. The experiment uses three different deep learning methods, Deep ML, Spectral DeseNet and Spectral-Spatial DenseNet for model training, and randomly selects samples as training and test data. Finally, the classification results of these three deep learning methods are compared with the classification results selected according to the NDVI and EVI thresholds of the vegetation index to test the classification ability of the model. | en_US |