Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.

Ghost imaging is widely used in underwater active optical imaging because of its simple structure, long distance, and non-local imaging. However, the complexity of the underwater environment will greatly reduce the imaging quality of ghost imaging. To solve this problem, an underwater ghost imaging method based on the generative adversarial networks is proposed in the study. The generator of the proposed network adopts U-Net with the double skip connections and the attention module to improve the reconstruction quality. In the network training process, the total loss function is the sum of the weighted adversarial loss, perceptual loss, and pixel loss. The experiment and simulation results show that the proposed method effectively improves the target reconstruction performance of underwater ghost imaging. The proposed method promotes the further development of active optical imaging of underwater targets based on ghost imaging technology.

No abstract

Investigations from past earthquakes have shown that underground subway stations can suffer excessive deformation under strong seismic loads, leading to the damage of critical components and the collapse of structures. This study presents the results of finite element analyses on the seismic damage of underground subway stations installed under different soil constraint conditions. The plastic hinge distribution and damage characteristics of cut and cover double-storey and three-storey subway stations are analyzed using the finite element method software ABAQUS. Combined with the static analysis results of the column sections, a discriminant method for bending plastic hinges is presented. The numerical results show that the collapse of the subway stations begins with the failure of the bottom columns’ bottom sections, which leads to the bending of the plates and the destruction of the whole structure. The bending deformation at the end section of columns has an approximatively linear relationship with the inter-storey drift ratio, and the change in soil conditions shows no apparent influence. The sidewall deformation behavior varies significantly under different soil conditions, and the bending deformation at the bottom section of sidewalls increases along with an increase in the soil-structure stiffness ratio at the same inter-storey drift deformation level. The sidewall bending ductility ratio of the double-storey and three-storey stations at the elastic-plastic drift ratio limit increases by 61.6% and 26.7%, respectively. In addition, the fitting curves between the component bending ductility ratio and inter-storey drift ratio based on the analysis results are also presented. The findings may provide a helpful reference for the seismic performance evaluation and design of underground subway stations.

No abstract