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Highway crack segmentation is a critical task for highway infrastructure monitoring and maintenance. While imagery from unmanned aerial vehicles (UAVs) is applied to the task of highway crack segmentation, it has great prospects in terms of speed and range. However, it is difficult to accurately identify road cracks from UAV remote sensing images, because the cracks are very narrow and small, often containing only a few pixels. To improve the segmentation of road cracks in UAV images, this study proposed an improved identification technique based on the U-Net architecture enhanced with a convolutional block attention module, an improved encoder and the strategy of fusing long and short skip connections. A public road crack dataset was relabelled for network training and a UAV remote sensing road crack dataset containing 1157 images was used to verify the generalisation ability of the enhanced network model. Results showed that the proposed method could effectively predict highway cracks in UAV images, with mIoU of 77.47 % and crack accuracy of 68.38%, which was better than the traditional U-Net model and some traditional semantic segmentation models. The proposed network is trained quickly by public dataset and can predict the road cracks on the new UAV images with high crack accuracy. This study provides an effective solution for the need to quickly grasp the damage status of roads over a wide area in the case of earthquake and other natural disasters. The highway crack segmentation benchmark dataset will be open sourced after the manuscript is accepted at: https://github.com/zhhongsh/UAV-Benchmark-Dataset-for-Highway-Crack-Segmentation.

Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance.

Deep learning detection methods use in ship detection remains a challenge, owing to the small scale of the objects and interference from complex sea surfaces. In addition, existing ship detection methods rarely verify the robustness of their algorithms on multisensor images. Thus, we propose a new improvement on the “you only look once” version 3 (YOLOv3) framework for ship detection in marine surveillance, based on synthetic aperture radar (SAR) and optical imagery. First, improved choices are obtained for the anchor boxes by using linear scaling based on the k-means++ algorithm. This addresses the difficulty in reflecting the advantages of YOLOv3's multiscale detection, as the anchor boxes of a single detection target type between different detection scales have small differences. Second, we add uncertainty estimators for the positioning of the bounding boxes by introducing a Gaussian parameter for ship detection into the YOLOv3 framework. Finally, four anchor boxes are allocated to each detection scale in the Gaussian-YOLO layer instead of three as in the default YOLOv3 settings, as there are wide disparities in an object's size and direction in remote sensing images with different resolutions. Applying the proposed strategy to ``YOLOv3-spp” and ``YOLOv3-tiny,” the results are enhanced by 2%–3%. Compared with other models, the improved-YOLOv3 has the highest average precision on both the optical (93.56%) and SAR (95.52%) datasets. The improved-YOLOv3 is robust, even in the context of a mixed dataset of SAR and optical images comprising images from different satellites and with different scales.

Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural...

Due to the limited number of implantable microelectrodes, subjects worn by retinal prosthesis received only limited discrete light spot (called artificial vision). Thus, the external abundant information is largely lost. Whereas there is little room for significant improvement in the electrodes number, it would be a feasible way to optimize the low-resolution information in the prosthetic vision by effective image processing. Based on luminance contrast, a real-time saliency detection strategy was presented to enhance the artificial vision, which included color space conversion and a visual attention simulation processing method. The strategy was evaluated by two benchmark databases, and its superiority was validated in extracting image foreground compared with the basic luminance contrast and other algorithms. Meanwhile, simulated experiments were conducted to verify the proposed strategy in the retinal prosthesis.

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy.

To investigate the static and kinematic positioning performance with the low-cost multiple Global Navigation Satellite System (GNSS) receivers is vital for the GNSS community owing to their wide civil applications. In this contribution, we developed the real-time kinematic (RTK) algorithms for low-cost GNSS receiving module to fulfil the task of the integrated RTK positioning with US Global Positioning System (GPS) and the Chinese BeiDou Navigation Satellite System (BDS). The RTK positioning performance of a low-cost module named STA8090 and a state-of-the-art module named Novatel OEM6 was compared. In our study, the virtual reference station (VRS) data stream from QianXun SI was utilized. First, one 12-h static test was performed, achieving the centimeter-level horizontal accuracy with an RMS (Root Mean Square) of less than 1 cm. Afterwards, some kinematic experiments were conducted under four different environmental scenarios, of which positioning accuracy of approximately 20 cm was obtained. The experimental results demonstrated that the RTK positioning performance with low-cost multi-GNSS module can fulfil the current navigation capability well.

The COVID-19 pandemic is the most serious catastrophe since the Second World War. To more accurately observe the epidemic under the influence of policies and provide policy adjustments before the official presidential transition in the United States, we use a three-layer superimposed Long-Short-Term-Memory (LSTM) model to predict the epidemic development trend to mid-January, 2021. The proposed model provides more accuracy and stability relative to Susceptible-Exposed-Infective-Recovered (SEIR), modified stacked au-to-encoder, and single-layer LSTM models. The performance effects of the measures in China and five countries with severe epidemics are analysed and summarised. The model shows that the error rate of China, five countries and the world is less than 1.4%. According to forecasts, the epidemic situations in the United States, India, and Brazil, caused by untimely, inappropriate policies, lax regulations and insufficient public cooperation, remain very severe, with cases continuing to increase by tens of thousands. The number of cumulative confirmed cases worldwide will exceed 84.58 million by mid-January, 2021; however, the mortality rate will gradually decrease. Based on analysis of measures (including China’s effective prevention and control policies), we found that there are performed tremendous different efficiency even using same positive policy for different countries because of various cooperation between people and governments. It is essential to maintain self-protection to prevent the epidemic from deterioration or regenerating, especially, wearing mask and maintaining a safe distance.

Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral-spatial-joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral-spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three-dimensional convolutional neural network (3D-CNN) model is designed to extract the deep spectral-spatial features of sea ice. The proposed method combines relevant textural features and performs spectral-spatial feature extraction based on the 3D-CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral-spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91%) with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images.

In various kinds of emergency, it's the proper decision-making and timely medical treatment that will effectively save lives and lower the damage to a minimum. Steady advances in wireless networking, medical sensor and interoperability software create exciting possibilities for improving the way of providing emergency care in prehospital situations. A real-time patient monitoring system based on the third generation (3G) cellular standard and General Packet Radio Service (GPRS) was designed and developed for emergency care and response management. Besides of vital signs of patients, such as ECG, SpO2 and NIBP, the system simultaneously transmits videos, voice and geographical position and so on, which is required throughout the prehospital procedure. Moreover, a hand-held ID card reader can automatically record the patient personal information, and the patient care software received the real-time patient data from the sensor and processes the data to detect anomalies. The testing results of the system show that it has the potential to improve the efficiency of the medical staff in the rescue operation for the most part.