PurposeNatural disasters are increasingly more frequent and intense, which makes it critical for emergency managers to engage social media users during crises. This study examined emergency official ...accounts' social media engagement at each disaster stage based on Fink's four-stage model of crisis and disaster: prodromal, acute, chronic and termination stages and linked topics and sentiments to engagement.Design/methodology/approachUsing text mining and sentiment analysis, 1,226 original tweets posted by 66 major emergency official Twitter accounts and more than 15,000 retweets elicited across the life cycle of Hurricane Irma were analyzed.FindingsResults identified the most engaging official accounts and tweets. Most tweets and the most engaging tweets were posted in the prodromal stage. Tweets related to certain topics were significantly more engaging than others. The most frequently tweeted topics by official accounts were less engaging than some seldom tweeted topics. Negative sentiment words increased the engagingness of the tweet. Sadness was the strongest predictor of tweet engagement. Tweets that contained fewer sadness words were more engaging. Fear was stronger in positively predicting tweet engagement than anger. Results also demonstrated that words for fear and anger were critical in engaging social media discussions in the prodromal stage. Words for sadness made the tweets less engaging in the chronic stage.Originality/valueThis study provided detailed instructions on how to increase the engagingness of emergency management official accounts during disasters using computational methods. Findings have practical implications for both emergency managers and crisis researchers.
SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep ...learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD’s official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 × 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.
Ship classification in synthetic aperture radar (SAR) images is a fundamental and significant step in ocean surveillance. Recently, with the rise of deep learning (DL), modern abstract features from ...convolutional neural networks (CNNs) have hugely improved SAR ship classification accuracy. However, most existing CNN-based SAR ship classifiers overly rely on abstract features, but uncritically abandon traditional mature hand-crafted features, which may incur some challenges for further improving accuracy. Hence, this article proposes a novel DL network with histogram of oriented gradient (HOG) feature fusion (HOG-ShipCLSNet) for preferable SAR ship classification. In HOG-ShipCLSNet, four mechanisms are proposed to ensure superior classification accuracy, that is, 1) a multiscale classification mechanism (MS-CLS-Mechanism); 2) a global self-attention mechanism (GS-ATT-Mechanism); 3) a fully connected balance mechanism (FC-BAL-Mechanism); and 4) an HOG feature fusion mechanism (HOG-FF-Mechanism). We perform sufficient ablation studies to confirm the effectiveness of these four mechanisms. Finally, our experimental results on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) jointly reveal that HOG-ShipCLSNet dramatically outperforms both modern CNN-based methods and traditional hand-crafted feature methods.
Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered with by low scattering backgrounds and cluttered noises, causing poor detection-tracking accuracy. ...Thus, a shadow-background-noise 3D spatial decomposition (SBN-3D-SD) model is proposed to enhance shadows for higher detection-tracking accuracy. It leverages the sparse property of shadows, the low-rank property of backgrounds, and the Gaussian property of noises to perform 3-D spatial three-decomposition. It separates shadows from backgrounds and noises by the alternating direction method of multipliers (ADMM). Results on the Sandia National Laboratories (SNL) data verify its effectiveness. It boosts the shadow saliency from the qualitative and quantitative evaluation. It boosts the shadow detection accuracy of Faster R-CNN, RetinaNet, and YOLOv3. It also boosts the shadow tracking accuracy of TransTrack, FairMOT, and ByteTrack.
Array synthetic aperture radar (SAR) 3-D imaging can obtain 3-D information of the target region, which is widely used in environmental monitoring and scattering information measurement. In recent ...years, with the development of compressed sensing (CS) theory, sparse signal processing is used in array SAR 3-D imaging. Compared with matched filter (MF), sparse SAR imaging can effectively improve image quality. However, sparse imaging based on handcrafted regularization functions suffers from target information loss in few observed SAR data. Therefore, in this article, a general 3-D sparse imaging framework based on regularization by denoising (RED) and proximal gradient descent-type method for array SAR is presented. First, we construct explicit prior terms via state-of-the-art denoising operators instead of regularization functions, which can improve the accuracy of sparse reconstruction and preserve the structure information of the target. Then, different proximal gradient descent-type methods are presented, including a generalized alternating projection (GAP) and an alternating direction method of multiplier (ADMM), which is suitable for high-dimensional data processing. Additionally, the proposed method has robust convergence, which can achieve sparse reconstruction of 3-D SAR in few observed SAR data. Extensive simulations and real data experiments are conducted to analyze the performance of the proposed method. The experimental results show that the proposed method has superior sparse reconstruction performance.
Sea cucumbers are widely known for their powerful regenerative abilities, which allow them to regenerate a complete digestive tract within a relatively short time following injury or autotomy. ...Recently, even though the histological changes and cellular events in the processes of intestinal regeneration have been extensively studied, the molecular machinery behind this faculty remains unclear. In this study, tandem mass tag (TMT)-based quantitation was utilized to investigate protein abundance changes during the process of intestine regeneration. Approximately 538, 445, 397, 1012, and 966 differential proteins (DEPs) were detected (
< 0.05) between the normal and 2, 7, 12, 20, and 28 dpe stages, respectively. These DEPs also mainly focus on pathways of cell proliferation and apoptosis, which were further validated by 5-Ethynyl-2'-deoxyuridine (EdU) or Tunel-based flow cytometry assay. These findings provide a reference for a comprehensive understanding of the regulatory mechanisms of various stages of intestinal regeneration and provide a foundation for subsequent research on changes in cell fate in echinoderms.
To realize the resourceful use of soilbags filled with graphite tailings, their load-bearing and deformation characteristics must be fully understood. In this study, the following results were ...obtained by performing geometric testing of water-filled sealing bags and uniaxial compression tests of soilbags filled with graphite tailings. The volume of the soilbag expressed in rectangular form was approximately 0.773 times the actual volume. The types of compression damage to soilbags can be defined as surface damage and overall damage. The surface damage load increases with decreasing filling density and decreases with decreasing soilbag size. Moreover, the higher the tensile capacity of the soilbag material and the lower the friction between the soilbags, the greater the surface damage load. The overall damage load increased with an increase in the tensile strength of the soilbag material and decreased with an increase in the degree of filling; the overall damage load was greater for large-sized soilbags at high degrees of filling. Thus, the existing theoretical calculation method cannot accurately calculate the damage load of soilbags filled with graphite tailings, and the test results deviate from the theoretical calculation results, with the latter showing an increasing damage load with a decreasing filling degree.
Nearfield (NF) 3-D imaging provides an effective solution of objects' radar cross section (RCS) within a compact range. This article proposes a precise RCS extrapolation via NF 3-D imaging with ...adaptive parameter optimization Bayesian learning (APOBL), i.e., first, in the process of NF 3-D imaging, objects' scattering centers may vary with the observation angle, while it is hard for the existing Bayesian learning via presetting parameters to reach an optimal estimation. For this issue, we present a parameter self-adaption solution, improving precision, and stability. Second, we also apply a block-based optimization idea in Bayesian-learning-based 3-D imaging, ensuring NF 3-D imaging quality. Third, in the process of RCS extrapolation, we apply a weighted Green's function operator into the 3-D imaging-based NF-to-far-field (NF-FF) compensation, further ensuring high precision. The simulation and experiment results verify that the proposed method has an advantage in precision over the existing 3-D imaging-based RCS extrapolation methods.
Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection ...community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology.
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