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Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

After a fire breaks out, pedestrians simultaneously move towards the exit and quickly form a crowded area near the exit. With the intensification of pedestrians' tendencies towards unfair competition, there is an increase in pushing and collisions within the crowd. The possibility of stampedes within the crowd also gradually increases. Analyzing the causes and psychological tendencies behind pedestrian pushing and collisions has a positive effect on reducing crowd instability and improving evacuation efficiency. This research proposes a modified social force model considering the unfair competition tendency of pedestrians. The model considers factors such as the gap between pedestrians' actual and maximum achievable speed, effective radius, and their distance from the exit. In order to overcome the shortage of "deadlock" in the classical social force model in a high-density environment, this research introduces the feature of variable pedestrian effective radius. The effective radius of pedestrians dynamically changes according to the density of the surrounding crowd and queuing time. Through validation, the evacuation efficiency of this model aligns well with the actual situation and effectively reflects pedestrians' pushing and squeezing behaviors in high-density environments. This research also analyzes how to strategically arrange obstacles to mitigate the exacerbating effect of unfair pedestrian competition on exit congestion. Five experiments were conducted to analyze how the relative position of obstacles and exits, the number of evacuation paths, and the size of the obstacle-free area before the exit affect evacuation efficiency in the presence of unfair pedestrian competition. The results show that evacuation efficiency can be improved when obstacles play a role in guiding or reducing the interaction of pedestrians in different queues. However, when obstacles hinder pedestrians, the evacuation efficiency is reduced to a certain extent. [ABSTRACT FROM AUTHOR]

Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.

Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline extraction method was proposed in this paper with an integrated Otsu threshold method, a region-growing algorithm, Canny edge detection, and a morphology operator. Based on SAR feature extraction and screening, the multi-scale segmentation method and KNN classification algorithms were used to achieve object-oriented automatic classification. According to different types of ground features, the correction criteria were presented and used in correcting the instantaneous waterline in biological coasts and undeveloped silty coasts. As a result, the accurate extraction of the coastline was accomplished in the area of the Yellow River Delta. The coastline was compared with that extracted from the GF-1 optical image. The result shows that the deviation degree was less than the field distance represented by three pixels. [ABSTRACT FROM AUTHOR]

In view of that difficulty of distinguish between sea fog and low cloud by optical remote sensing mean, the research on spect ral characteristics of sea fog is focused and carried out? The satellite laser radar CALIOP data and the high spectral MODIS data were obtained from May to December 2017, and the scattering coefficient and the vertical height information were extracted from the atmospheric attenuation of the lower star to extract the sea fog sample points, and the spectral response curve based on MODIS was formed to analyse the spectral response characteristics of the sea fog, thus providing a theoretical basis for the monitoring of sea fog with optical remote sensing image. [ABSTRACT FROM AUTHOR]

In this paper, the Cryosat-2 altimeter data distributed by the ESA, and these data are processed to extract the information of the elevation change of the Antarctic ice sheet from 2010 to 2017.Firstly, the main pretreatment preprocessing for Cryosat-2 altimetry data is crossover adjustment and elimination of rough difference. Then the grid DEM of the Antarctic ice sheet was constructed by using the kriging interpolation method, and analyzed the spatial characteristic time characteristics of the Antarctic ice sheet. The latitude-weighted elevation can be obtained by using the elevation data of each cycle, and then the general trend of the Antarctic ice sheet elevation variation can be seen roughly. [ABSTRACT FROM AUTHOR]