The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

The ship detection task using optical remote sensing images is important for in maritime safety, port management and ship rescue. With the wide application of deep learning to remote sensing, a series of target detection algorithms, such as faster regions with convolution neural network feature (R-CNN) and You Only Look Once (YOLO), have been developed to detect ships in remote sensing images. These detection algorithms use fully connected layer direct regression to obtain coordinate points. Although training and forward speed are fast, they lack spatial generalization ability. To avoid the over-fitting problem that may arise from the fully connected layer, we propose a fully convolutional neural network, SDGH-Net, based on Gaussian heatmap regression. SDGH-Net uses an encoder–decoder structure to obtain the ship area feature map by direct regression. After simple post-processing, the ship polygon annotation can be obtained without non-maximum suppression (NMS) processing. To speed up model training, we added a batch normalization (BN) processing layer. To increase the receptive field while controlling the number of learning parameters, we introduced dilated convolution and added it at different rates to fuse the features of different scales. We tested the performance of our proposed method using a public ship dataset HRSC2016. The experimental results show that this method improves the recall rate of ships, and the F-measure is 85.05%, which surpasses all other methods we used for comparison.

Based on DMSP/OLS nighttime light data, we select the Yangtze River Economic Belt (YREB) as the research area to evaluate the urbanization level of the YREB at provincial, regional, and county scales. First, we construct the compounded night light index (CNLI) based on the nighttime light data by extracting the integrated light intensity (I) and the built-up area (S) of the Yangtze River economic belt from 1992 to 2012, and we respectively calculate the CNLI of the YREB at the province, regional and county scales of the YREB such that we can comprehensively analyze the spatial and temporal distribution characteristics and dynamic changes of the process of urbanization in the YREB. Second, we construct two traditional urbanization level evaluation index models based on socio-economic statistics: The integrated urbanization level index model (IU) and the urbanization composite index model (Cf), which are used to respectively calculate the urbanization levels at the province, region, and county scales of the YREB. Finally, we establish a multi-scale urbanization model of CNLI, IU, and Cf in order to perform correlation analysis of CNLI, IU, and Cf on different scales. The results show that CNLI can objectively reflect the development status of urbanization level, and provide a new index and method for better monitoring the state of urban development and evaluating the annual change of urbanization.