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The deep sea has abundant resources and is regarded as the "sixth continent" that humans can use. Although research on the deep sea has made some progress, its exploration is not deep enough. The acquisition and analysis of deep-sea images can provide important technical means for deep-sea exploration and the acquisition of information. However, there is currently no complete underwater image dataset. For this reason, this paper is the first deep-sea visual dataset of the South China Sea, contains over 100 TB videos and 1 million high quality images. This paper also applies the dataset to underwater image enhancement and image superresolution, and the experiment proves the effectiveness of the dataset. [ABSTRACT FROM AUTHOR]

The study of ultrasonic image deblurring is of high research value and practical significance, which can provide significantly useful medical pathological information for doctors and patients. Ultrasonic images are prone to speckle noise or blur. Real-time deblurring of ultrasonic images is a common problem in medical circles in China and globally. In this paper, based on the causes of ultrasound image degradation and its degradation model, the problem of ultrasound image deblurring is studied in depth. A new regularization constraint model is given, and the algorithm of ultrasound image deblurring with double constraints is effective. Through the experiment, the image quality evaluation index is used to evaluate the experimental results. The improved algorithm proposed in this paper improves the peak signal-to-noise ratio by 7%, the structural similarity value by 6%, the visual information fidelity by 14%, the feature similarity value by 4%, the resolution gain by 4%, and the new blind image quality evaluation value by 22%. The key information of the image can be kept faithfully. Compared with the traditional methods, the proposed method enhances the identification of the focus area in ultrasound images, provides a fast, efficient and complete deblurring method for medical image analysis, and faithfully retains and restores the required information in ultrasound images. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to discuss the feasibility of utilizing the non-explosive source green seismic exploration technology to detect mineral resources in the deep of complex mountainous areas of western and southern China as well as to provide a feasible detection scheme. Based on seismic illumination analysis and prestack depth-domain migration experiments, a viable active-source seismic detection system was designed for the Zhuxi mining area of Jingdezhen. Geophysical models were established according to the combined drilling profile of L42 and the corresponding petrophysical measurements of rocks based on geological findings, drilling results, and site investigations. Since the single-component p-wave seismometers would be primarily utilized for the subsequent seismic data acquisition, the viscous-acoustic wave equation was selected to simulate the propagation of the seismic wave. Meanwhile, the acoustic-elastic boundary approach (AEA) was used to solve the irregular free-surface problem caused by the rugged Earth's surface. Seismic source-receivers bidirectional illumination analysis and prestack depth-domain reverse time migration experiments were performed on simulated seismic data with 20 dB high SNR (signal-to-noise ratio) and 6 dB low SNR, respectively. The results of this study showed that by adopting a dense exciting-receiving seismic detection system, the eco-friendly non-explosive seismic source technology could reveal the spatial distribution of the main strata underneath the target area and the ore-controlling structures like faults and overthrust nappes. Based on this study, the next step of seismic data acquisition was implemented. The first-hand non-explosive source seismic profiles of the Zhuxi mining area demonstrated that the detection scheme designed was feasible and effective. By constantly improving the technical system, the eco-friendly non-explosive active-source technology would provide a safe and efficient approach, which could be used in the research fields of deep mineral resource prospection, buried faults exploration, urban underground space detection, etc. Finally, the proposed design and demonstration of an active-source seismic detection system based on illumination analysis and migration experiments merits further study and applications. [ABSTRACT FROM AUTHOR]

This article aims to present a Mixed Integrative Heuristic Approach (MIHA) for image studies based on textual content (media news) analysis. MIHA was developed during an exploratory study on the image of China and the Chinese in Zambia and Angola. The analyzed database was relatively small (2477 articles), and the material was very heterogeneous, because of the wide scope of topics covered by the texts and the fact that we analyzed content from both English- and Portuguese-language media. To obtain the best possible results, we decided to employ the mixed methods approach for a bottom-up created dictionary, and to design a set of sentiment indexes based on both automatic and manual coding. This paper focuses on the two most important steps of the method development: dictionary building and sentiment indexes design. [ABSTRACT FROM AUTHOR]

Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing. [ABSTRACT FROM AUTHOR]

This paper will provide a case study to show how the psychoanalyst can use the intuitive archetypal image from a client in the analytic process. The animal image occurred in the analysis has been amplified beyond personal, cultural, historical associations as a ¨fox¨ in modern China. The fox image represents an archetype of masculine energy emerged from female agency as the animal instinct to break through tradition and the limitations of culture and gender. [ABSTRACT FROM AUTHOR]

At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency but also produce large errors. To solve the above problems, this paper proposes an image colour data element analysis model based on depth neural network. Firstly, intelligent analysis of image colour data elements based on tensorflow is constructed, and the isomerized tensorflow framework is designed with the idea of Docker cluster to improve the efficiency of image element analysis. Secondly, considering the time error and spatial error diffusion model in the process of image analysis, the quantization modified error diffusion model is replaced by the original model for more accurate colour management. Image colour management is an important link in the process of image reproduction; the rotating principal component analysis method is used to correct and analyze the image colour error. Finally, using the properties of transfer learning and convolution neural network, an image colour element analysis model based on depth neural network is established. Large-scale image data is collected, and the effectiveness and reliability of the algorithm are verified from different angles. The results show that the new image colour analysis method can not only reveal the true colour components of the target image; furthermore, the real colour component of the target image also has high spectral data reconstruction accuracy, and the analysis results have strong adaptability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, climate change in the Jinping area is investigated. The climate change trend in the Jinping area is studied by plotting the porosity value of the carbonate rocks as a curve. By comparing the curve established using the climate change data from published articles, it is found that the B value curve obtained using the saddle line is the closest to the curve established using the climate change data from published articles. This shows that the carbonate porosity in the Jinping area obtained using an image analysis technique can be used for climate change research. [ABSTRACT FROM AUTHOR]

Copyright of New Architecture is the property of New Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On 28 August 2017, a large catastrophic rock avalanche occurred in Nayong, Guizhou Province, China. It claimed 35 lives and caused large property losses. The Nayong rock avalanche provides a rare opportunity to infer the dynamic fragmentation of rock masses from deposits. Field surveys combined with an uncrewed aerial vehicle image analysis system were performed to investigate the particle size and shape distribution along the runout path of the Nayong rock avalanche. The Weibull particle size distribution (PSD) model was tested and possesses the highest accuracy and capability over the entire range of the Nayong rock PSD, and it shows that the median size (D50) values of the particles range from 0.16 to 16.31 m along the runout path. Moreover, the evolution of D50 along the runout path confirms that the degree of rock fragmentation varies greatly in different regions. Additionally, the relative breakage (BR) of particles in the deposition area is 0.191. Particle R values increase significantly from 0.55 to 0.78 along the runout path, which indicates that the rock grinding (shape change) process continuously occurs throughout the avalanche path. The image analysis results confirm that the dominance of rock fragmentation and grinding alternates during granular flow transport. Based on these results, the classic fragmentation‐spreading model for long‐runout rock avalanches is optimized by taking into account the method by which grinding rocks increases the speed of avalanches. Plain Language Summary: On 28 August 2017, a large catastrophic rock avalanche occurred in Nayong, Guizhou, China. In this paper, a field survey based on High‐resolution uncrewed aerial vehicle technology and image‐analysis system was performed to study the characteristics of particle size and shape distribution and the long‐runout behavior of large rock avalanches. A fragmentation‐spreading model for long‐runout behavior of rock avalanches proposed by Davies was optimized by incorporating the mechanisms involving internal friction reduction caused by dynamic grinding process. The internal friction of particles dropped correspondingly during transport and resulted in the increase of spreading distance. Key Points: An image analysis system for the large‐scale field investigation has been applied to morphometric measurements of rock avalanchesThe degree of rock fragmentation gradually decreases with the travel distance of rock mass in a rock avalancheRock grinding gradually replaces the role of rock fragmentation and reshapes the particles as the travel distance increases [ABSTRACT FROM AUTHOR]

This paper investigates the construction and representation of the identity of mingong ("peasant/migrant workers") with regard to its cinematic images in contemporary Chinese films. Specifically, it analyses main protagonists' transformation from xia ("knight-errant") to canxia ("impaired knight-errant") and their actions as a form of tragic heroism in Jia Zhangke's A Touch of Sin (2013). As a fundamental labor force that supplements the rapid growth of global economy and a crucial element in China's economic rise, these new urban subjects have created new class divisions and social inequality and hence important contradictions in China's latest drive toward modernization. They also articulate a radical and localized critique of globalization. As a result of the primitive accumulation of capitalist accumulation, migrant workers are impelled to participate in the global market and become one invisible and automatized segment on the "assemblage line." Deleuze and Guattari's assemblage theory servers as the theoretical framework here and lays bare the multidimensional feature of the construction and representation of the identity of mingong. In conclusion, the analysis of the cinematic images of mingong in Jia's film is a necessary complement to the study of contemporary Chinese migrant workers as well as the politics of trafficking bodies. [ABSTRACT FROM AUTHOR]

The great M ~ 8 1920 Haiyuan earthquake (HYEQ) was one of the largest and most deadly earthquakes in China in the last century, with ~ 234,000 deaths. The earthquake occurred within the Loess Plateau of northern China, where Quaternary loess deposits form a distinctive blanket across the landscape. Large regions of this loess cover experienced co-seismic landslides. Based on an analysis of the original disaster reports, field surveys, and satellite image interpretation, we have compiled the shaking effects of the earthquake, including the distribution of landslides, fatalities, and structural damage. Landslides triggered by the HYEQ (n > 7,000) are concentrated south of the Haiyuan fault, in a region that has both thick loess cover and long-term relief generated by the drainage network. This distribution is spatially separated from landslides triggered by other earthquakes. We find that in contrast to previous studies, the most important factor in the severe death toll of the HYEQ was the collapse of housing by ground shaking, including collapse of loess house-caves. Landslides were a secondary factor; although up to 32,000 deaths occurred in areas with intense landsliding. Based on the revised distribution pattern of landslides and damage (e.g., house collapses), we suggest that the isoseismal intensity IX line extends south of previous locations. We have also identified 126 dammed lakes created by co-seismic landslides, which form major modifications of this semi-arid landscape. The research methods in this paper, combining historical review, satellite image interpretation, and field validation of landslides, can be used as a reference for studies of other areas affected by historical earthquakes and co-seismic landslides, elsewhere in the Loess Plateau and beyond. [ABSTRACT FROM AUTHOR]

The research results of this paper provide new ideas and perspectives for the construction of trading market of China's carbon emission rights, so as to better alleviate the difficulties in China's energy conservation and emission reduction. Data evaluation and image analysis based on wavelet transform image processing technology and deep learning were used in the study. Moreover, wavelet transform was used to extract the edge, and compared with the results of several other methods for extracting edges, so as to further study and analyze the trend of carbon emissions trading. The fluctuations in the price of carbon emissions trading, the shift of state, and the period of ups and downs are affected by many random factors. Through research algorithms, we can estimate the situation, seize opportunities, and forecast the prospects. In addition, based on the research, this paper obtains the relevant strategies for carbon emissions trading and value assessment in China. [ABSTRACT FROM AUTHOR]

Chinese materia medica resource survey provides an important basis for the development of traditional Chinese Medicine (TCM) industry. During the Chinese materia medica resource survey process, millions of materia medica plant images are collected. The collected image dataset includes some images that are unqualified for image analysis, i.e. they can’t be used to build medicinal plant classifier model. It is a burdensome work to identify the unqualified Chinese materia medica resource images manually. How to screen the unqualified images automatically is an important task of Chinese materia medica resource survey. Image recognition techniques developed quickly in recent years. Outlier detection is a kind of unsupervised method to find the unqualified images automatically. Lots of research work has been done on the topic. Extracted features and correlation metric play important roles on the outlier image detection result. For improving the image screening performance, a novel outlier detection method is proposed in this paper. Convolutional neural network (CNN) is used to extract the complicated features of Chinese materia medica resource images. Extended entropy is introduced into the calculation of information loss that is used to measure the distance between images. Based on the extracted image features and correlation metric, a novel outlier detection method based on clustering is proposed here. The efficiency of the screening method is illustrated with a practical example. [ABSTRACT FROM AUTHOR]

Landslide extraction is one of the most popular topics in remote sensing. Numerous techniques have been proposed to manage the landslide identification problem. However, most aim to extract landslides that have already occurred or delineate the potential landslide manually. It is greatly important to identify and delineate potential landslides automatically, which has not been investigated. In this paper, we propose an automatic identification and delineation method, i.e., object-based image analysis (OBIA) of potential landslides by integrating optical imagery with a deformation map. We applied a deformation map generated by the interferometric synthetic aperture radar (InSAR) technique, rather than the digital elevation model (DEM) for landslide segmentation. Then, we used a classification and regression tree (CART) model with the spectral, spatial, contextual and deformation characteristics for landslide classification. For accuracy assessment, we implemented the evaluation indicators of recall and precision. The proposed method is verified in both specific landslide-prone regions (Jinpingzi and Shuanglongtan landslides) and a large catchment of the Jinsha River, China. By comparing our results with the ones using purely optical imagery, the precision of the Jinpingzi landslide is improved by 14.12%, and the recall and precision of the Shuanglongtan landslide are improved by 3.1% and 3.6%, respectively, and the recall for the large catchment is improved by 9.95%. Our method can improve delineation of potential landslides significantly, which is crucial for landslide early warning and prevention. [ABSTRACT FROM AUTHOR]

Medical electrical impedance tomography (EIT) reconstruction algorithms are approximately forty years old and are now being used to treat patients. At the same time, many advanced concepts are still active research areas, and literature regarding EIT algorithms has been presented in different professional journals and scientific conferences. Bibliometrics analysis method is used to assess the 30 years of scientific output and activity regarding EIT algorithm research from a global perspective, covering global publications, cooperation between countries, and clustering of keywords. 2467 research articles, reviews, letters, and proceeding papers published from 1987 to 2021 in English on EIT reconstruction algorithm were analyzed. Literature statistics show that the worldwide growth trend is rising, and global geographic distribution of publications in this field indicates that USA, South Korea, and China ranked as the top three prolific contributors in terms of EIT algorithm. "inverse problem" "regularization""electrode models" were research focus. Overall, EIT algorithms bibliometric research provides the current hotspots and future directions of algorithm research. [ABSTRACT FROM AUTHOR]

It is common to see drivers and passengers not wearing seat belt in China, which is a traffic violation, thereby to strengthen supervision by resorting to advanced technical means is imminent. In the existing research, the detection effect is favorable when there is visible difference between the seat belt and the background image. However, in the opposing situation, it can easily lead to missed detection or mis-detection, and greatly reducing the detection accuracy and making it difficult to meet the demands of intelligent and automatic traffic monitoring. Study on the images of drivers and passengers wearing seat belt in traffic inspection covers three aspects: vehicle target positioning, driver and passenger area positioning and seat belt wearing behavior decision. Vehicle target positioning studies the enhanced vehicle features and looks for a vehicle classification model that is suitable for enhancing the features, and obtains the determined vehicle area from the study detection. Driver and passenger area positioning studies a multi-level joint supervised learning method according to the inclusive relationship between the non-rigid human body and the rigid vehicle body, and gets the optimal driver and passenger area by designing the multi-level learning classifier. Seat belt wearing behavior decision studies from the method of linear target recognition and uses the perceptual grouping mechanism of human visual system for robust seat belt detection. In the experimental phase, we validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out Seat Belt detection accurately and effectively. Compared with other Seat Belt target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of Seat Belt in intelligent transportation. [ABSTRACT FROM AUTHOR]

Nowadays, the number of vehicles in China has increased significantly. The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need. However, the existing early warning devices such as geomagnetic, ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance. In addition, geomagnetic detection will damage the road surface, while ultrasonic and infrared detection will be greatly affected by the environment. Considering the shortcomings of the existing solutions, this paper puts forward a solution of early warning for vehicle turning meeting based on image acquisition and microcontrollers. This solution combines image acquisition and processing technology, which uses image sensor to perceive traffic condition and image data analysis algorithm to process perceived image, and then utilize LED display screen to issue an early warning. [ABSTRACT FROM AUTHOR]

With the rapid development of economy and society and the continuous optimization of industrial structure, the demand for high skilled talents is increasing. Education plays an irreplaceable role in China's economic growth. Therefore, it is of great significance to study the impact of talent supply of vocational education on economic growth from the perspective of human capital and comprehensively consider the spatial interaction of economic growth. Taking intelligent image recognition technology as the main research technology, this paper discusses the contribution of educational human capital to regional green economic growth. This paper expounds the content of intelligent image recognition technology, constructs an image recognition system based on neural network, and studies the relationship between human resource utilization efficiency and regional economy under intelligent image recognition technology based on the empirical analysis of intelligent image recognition technology. Finally, it makes an empirical study on the spatial effect in the image recognition system and expounds the relationship between feature space and economic growth. It verifies the relationship between educational human capital and green economic growth. The results show that the intelligent image recognition technology has a good effect in the research of spatial effect. [ABSTRACT FROM AUTHOR]

Featured Application: The obtained kinds of borehole camera images of long or deep holes in the tunnels of the building Sichuan-Tibet Railway. Digital panoramic borehole imaging technology has been widely used in the practice of drilling engineering. Based on many high-definition panoramic borehole images obtained by the borehole imaging system, this paper puts forward an automatic recognition method based on clustering and characteristic functions to perform intelligent analysis and automatic interpretation researches, and successfully applied to the analysis of the borehole images obtained at the Wudongde Hydropower Station in the south-west of China. The results show that the automatic recognition method can fully and quickly automatically identify most of the important structural planes and their position, dip, dip angle and gap width and other characteristic parameter information in the entire borehole image. The recognition rate of the main structural plane is about 90%. The accuracy rate is about 85%, the total time cost is about 3 h, and the accuracy deviation is less than 4% among the 12 boreholes with a depth of about 50 m. The application of automatic recognition technology to the panoramic borehole image can greatly improve work efficiency, reduce the time cost, and avoid the interference caused by humans, making it possible to automatically recognize the structural plane parameters of the full-hole image. [ABSTRACT FROM AUTHOR]

Sports dance originated from the international standard national standard dance. Since China formally established the "China International Ballroom Dance Association" in 1986, it has developed rapidly. At the same time, related research on sports dancers has become a hot spot. However, most of the current researches are limited to athletes' physical training and competitive skills, and the research on athletes' dietary nutrition and athletic ability is still blank. In response to this situation, this paper presents a study on the relationship between nutritional diet and athletic ability of sports dancers based on image analysis of visual sensors. This article is mainly divided into three parts. The first part is the basic theories and core concepts of related research. This part introduces the modes and algorithms of visual sensors, analyzes the specific problems of current sports dance athletes in my country in terms of dietary nutrition, and continues with the adverse effects. It affects the athlete's athletic ability and even threatens the athlete's health. Improving the diet of athletes and establishing healthy dietary standards are of great significance to the development of this field. The second part is the establishment method of the nutritional intervention experiment model, which gives the principle and specific operation steps of the experiment design in detail. The third part is a comparative test. To further confirm the influence of nutrition on dance performance in athletes, this article conducted a number of comparative studies such as immunoglobulin conversion after intervention, changes in the indicators of lipid metabolism after intervention, and dietary mineral intake. Through the analysis of experimental data, it can be seen that reasonable dietary supplementation has a positive effect on the recovery of athletes' physical fitness, which can enhance the body's immunity while improving athletic ability. [ABSTRACT FROM AUTHOR]

It is critical to accurately assess the location and evolution of impending landslide hazards, especially when influenced by the effects of climate change. However, most existing landslide models only analyze the stability of individual grid cells without considering soil interaction between adjacent cells. This means that the stability of individual grid cells is not necessarily representative of actual slope stability at a given location, in turn affecting the accuracy of landslide stability assessments. In this paper, slope units are defined using the morphological image analysis and homogeneous slope unit method to represent the actual slope conditions and to extract the profile of each slope unit. The critical slip surface of the soil mass is investigated to account for the interaction between adjacent grid cells within the slope unit. A probabilistic analytical landslide model is established and the response mechanism of the hydrological processes is proposed. This model is applied to landslide hazard assessment in the Three-Gorges Reservoir Area, Fengjie County, China. Using historical rainfall data, the distribution of landslide hazards is analyzed for different antecedent precipitation levels and rainfall return periods. The results show that the amount of antecedent precipitation, and rainfall return period, have different effects on landslide triggering in the area. Importantly, the model reveals that extreme rainfall events induced by climate change will lead to a large number of new landslides in areas where they are not anticipated. Further investigation and monitoring (e.g., early-warning systems, long-term assessment) of rainfall-induced landslides in the Three-Gorges Reservoir Area is recommended. [ABSTRACT FROM AUTHOR]

Drawing on a combined framework of Appraisal System and Ideological Square Model, this paper conducts a corpus-based investigation of the ways in which the image of China is (re)shaped in the English translation of Chinese political discourse in terms of appraisal epithets. The results show that (1) shifts regularly occur in the English translation of the appraisal epithets in Chinese political discourse, though an equivalent translation strategy is a canonical option for the translators of Chinese political discourse; (2) translation patterns of the appraisal epithets vary within the three sub-categories of Appraisal System, with shifts found mostly in the translation of the negative appraisal epithets under 'engagement' and 'graduation' subcategories; (3) discursively, China is more negatively represented in the translated than in the source Chinese texts. A two-layered Ideological Square Model is proposed to account for the research findings in terms of ideological factors in the translation of Chinese political discourse. [ABSTRACT FROM AUTHOR]

In recent years, in order to study the micro-pore structures of reservoirs, to define the types of reservoir space and to classify and evaluate reservoir capabilities, the technology of numerical image analysis has been concerned widely with the study of pore heterogeneity with fractals and multifractals. This is of great challenge due to developments in image resolutions and quantitative characterization methods. In this paper, the pore structure of digital casting thin-section images of six samples of volcanic and volcano-sedimentary rocks from the Dagang area (Huanghua depression, China) was studied, and then the pore spaces of the reservoir samples were characterized automatically using multifractal parameters advantageous to describe the irregularity and disorder of geometric objects. The method of moments was used to derive the parameters of multifractality and heterogeneity of pore structures. The results show well that, together with cluster analysis, 12 fractal and multifractal parameters can be applied to describe effectively the irregular degrees of pore spaces. The six rock samples have been classified into three groups with various reservoir capabilities. The work presented here can provide new avenues for analysis of the spatial arrangement of mineral particles, aggregates and pores in spaces, and that the multi-scaling nature of casting thin-section images of rocks suggests using these algorithms as a basis for reservoir capability assessment. [ABSTRACT FROM AUTHOR]

Satellite image classification is the core of satellite image interpretation. The traditional shallow learning classification algorithm is difficult to extract remote sensing image features effectively, resulting in low classification accuracy. The deep neural network can automatically realize the feature analysis. However, due to the similarity of the optical properties of the cloud and snow, the existing deep learning model is difficult to obtain satisfactory results for the cloud and snow classification in the plateau. Meanwhile, too many parameters of deep learning lead to slow detection speed, which is difficult to meet the real-time requirements of detection. Furthermore, the deep learning method is prone to model degradation. In view of the existing problems, this paper proposes a dilated multi-scale cascade forest method to realize the classification of satellite cloud images. Multi-scale scanning increases the diversity of feature vector extraction and improves the separability of features. The dilated structure is designed to expand the feature receptive field, it improves the efficiency of feature extraction without losing the texture and spatial correlation information of the original image. In addition, the number of layers of network cascading does not need to be customized, it can be determined automatically according to the performance of the system. The proposed method effectively improves the accuracy of cloud/snow classification. Experiments on the imagery of HuanJing satellites A and B (HJ-1A/1B) in China demonstrate that the proposed model can get better classification results than existing studies. The time complexity of the proposed model is lower than existing works, hence it is practical in real-time and accurate remote sensing image classification. [ABSTRACT FROM AUTHOR]

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The selection and quality of the evaluation unit directly affect the rationality of the evaluation results of regional landslides, in which the slope unit as the evaluation unit can better describe the landform characteristics and topographic changes, and can better reflect the natural environmental conditions of landslides. However, there is currently no optimization method for slope unit partitioning, which restricts the application of slope units in regional landslide disaster evaluation. Therefore, this paper proposes a R-MSS (Refined Multi-Scale segmentation) method based on object-based image analysis (OBIA). Typical mountainous areas in southwest China and hilly areas in southeast China were selected and compared using hydrological analysis, curvature watershed, and MSS methods. The results show that the R-MSS method can effectively extract large-area slope units without a lot of manual modification. The divided slope units have uniform size, small spatial difference and good morphological effect. The overall effect is significantly better than other methods, especially in the high and steep mountainous areas with complex and changeable terrain, its segmentation performance is especially outstanding. The research results can serve for regional landslide hazard evaluation and improve the reliability of regional landslide hazard analysis results to a certain extent. [ABSTRACT FROM AUTHOR]

Abstract With the development of the game industry, and the new electronic consumption chain to promote the development of the game industry model continues to strengthen. Therefore, it is more necessary to make good use of good technology to do a good job of games. From the momentum of the development of mobile terminal games in recent years, the game industry has become a new economic growth point in China's economic transformation. With the development of computer and Internet, graphics and image processing technology has entered another unprecedented stage. In recent years, different image processing techniques have been used to process and analyze the game interface. Appropriate image processing methods include image enhancement, image binarization, image edge detection and image feature extraction. In this paper, by changing the mapping function from priority to probability and comparing the single mapping function of the previous algorithm, the mapping function of playback learning with higher probability of the important priority playback unit is found. In the experiment, the intuitive model strategy analysis of the improved algorithm is carried out firstly. Then the choice of CNN network layer structure, cost function analysis, efficiency analysis and game score comparison of each algorithm are carried out. Finally, the test results show that the new algorithm in this paper can make more effective decisions in video games and achieve the goal of winning higher scores and spending less time. [ABSTRACT FROM AUTHOR]

Previous studies on optical remote sensing mapping of landslides mainly focused on new landslides that have occurred, but little attention was paid to the early landslide due to its high concealment. InSAR technology, a prevalent method to detect early landslides, only can be used to identify the potential hazards of slow deformation. Therefore, it is necessary to explore new method of early landslides mapping by integrating all types of direct and indirect early features, such as cracks on slopes, small collapses inside and topographic features. In this study, an object-oriented image analysis method based on slope unit division and multi-scale segmentation was proposed to obtain accurate location and boundary extraction of early landslides. In the middle- and small-scale segmentation, the object, texture, spectrum, geometric features, topographic features, and other features were obtained to determine the local feature location of early landslides. The slope unit boundary was combined with the feature of a large-scale segmentation object to determine the scope of landslides. This method was tested in the Xianshui River basin in the Daofu County, Sichuan Province, China. The results demonstrate that: (1) Such features as landslide cracks and the small collapse at the bottom of slope can effectively determine the landslide position. (2) The slope unit division and the correct setting of shape factors in multiple segmentation can effectively determine the landslide boundary. (3) The accuracy of landslide location extraction was 83.33%, and the accuracy of boundary extraction for early landslides that were completely identified was evaluated as 82.67%. It is indicated that this method can improve the accuracy of boundary extraction and meet the requirements of the early landslides mapping. [ABSTRACT FROM AUTHOR]

Although the portrayal of Zhunti Daoren (Cundī Daoist 準提道人) in the Canonization of the Gods (Fengshen yanyi 封神演義) is relatively subdued, it has garnered considerable scholarly interest owing to the pivotal role he fulfills in advancing the narrative and the profound cultural connotations encapsulated within the character. Scholars have put forward three distinct hypotheses: "Zhunti Daoren as a Buddhist deity", "Zhunti Daoren as a Daoist deity", and "Zhunti Daoren as the Panchen Lama". Based on the theory that the figures in the novels of gods and demons (Shenmo xiaoshuo 神魔小說) embody symbolic characteristics, as well as the sociocultural background of Ming–Qing times, this study uses literary image analysis to investigate these three different interpretations. A basic examination is also performed to explore the correlation between Zhunti Daoren and Jieyin Daoren (Welcoming Daoist接引道人), with the aim of illustrating the relationship between the cult of Zhunti and Pure Land Buddhism in late imperial China. [ABSTRACT FROM AUTHOR]

Amidst global health concerns such as COVID-19 and rising obesity rates, the connection between food choices, societal well-being, and cultural shifts has gained attention. In China, the rise of "light food" consumption on platforms like Taobao reflects a growing trend towards health-conscious dietary preferences. With a dataset comprising 633 images and 45,817 words, condensed into 50 images for analysis, this research aims to understand how these advertisements influence perceptions of healthiness and desirability, and whether there is a discrepancy between advertised health benefits and actual nutritional content. This study delves into the advertising strategies employed for light food products on the Taobao platform in China, utilizing a Textual–Visual Thematic Analysis (TVTA) framework. Noteworthy findings include: (1) Internet-based light food advertising adopts a dual textual and visual approach. (2) Identification of prevalent multimodal devices, such as medium shots, varied camera angles, vibrant colors, and large size. (3) Uncovering inconsistencies in presentation that may pose potential consumer misperceptions. These discoveries underscore the nuanced landscape of light food advertising, offering valuable insights for both academia and industry. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Corona Virus 2 or SARS-CoV-2 (COVID-19) has affected 21 million people worldwide and is responsible for 0.75 million deaths (as of August 2020). Declared as a pandemic by the WHO, the virus has affected almost every country after originating in China articulating how contagious the virus is. With reasonable social measures, countries have already shown depreciation in coronavirus cases. This paper deals with detecting and distinguishing the COVID-19 disease from normal patients through frontal chest X-ray scans using Convolutional Neural Networks. Different combinations of parameters were used to train multiple CNNs and their behavior was noted. After thorough experimentation of different models, the best model which achieved an accuracy of 0.98 on the test set with a loss of 0.036 was nominated. The selected model's more intuitive accuracy metrics were calculated and its intermediate convolutional neural activations and receiver-operating characteristics curve is shown. [ABSTRACT FROM AUTHOR]

With the rapid development of tunnel engineering in China, geological conditions encountered in tunnel construction are becoming more and more complex. Construction safety has become the top priority of tunnel construction. To avoid geological hazards such as landslides, water inrush, and mud gushing caused by blind construction, ground penetrating radar (GPR) is widely used in the medium distance ahead of a tunnel face. The advanced prediction of GPR is often interfered by factors such as environment, operation, data processing, image interpretation, and other factors, which cause final results to have errors. Therefore, it is highly necessary to track and test prediction effects, and also, to choose scientific and effective evaluation methods for accuracy of the prediction result. Based on the geological radar forecast data of the Yujiashan tunnel, this paper sums up the interference factors of the ground radar forecast and discusses the advantages and disadvantages of different evaluation methods. On this basis, the analytic hierarchy process (AHP) which has many applications in earth sciences, physics, nanotechnology and etc., is improved by integrating the principle of maximum membership of fuzzy comprehensive evaluation. Then, a four-level evaluation model is established, which judges the accuracy of GPR geological prediction. Meanwhile, two geological predictions in the Yujiashan tunnel are selected to be applied in the model. Finally, the recognition level of the accuracy evaluation model is more accurate. The comparison between the actual excavation exposure and the radar image interpretation results shows that the geological radar advance prediction results are more accurate. This verifies the rationality and applicability of using the improved AHP to evaluate the accuracy of geological radar advance prediction. [ABSTRACT FROM AUTHOR]

Large-scale microwave remote sensing research requires multiple synthetic aperture radar (SAR) image mosaics. However, owing to multiple factors, different SAR images frequently exhibit intensity differences. This leads to poor intensity continuity after mosaicking, making SAR image interpretation extremely difficult. The existing method assumes that SAR images have been subject to strict radiometric calibration and mainly focuses on intensity balancing to minimize the intensity difference between adjacent images. This method is not effective when the intensity difference between SAR images is large. In this paper, a novel random cross-observation intensity consistency (RCOIC) algorithm is proposed to eliminate the intensity differences for SAR image mosaics. The proposed algorithm is based on global intensity constraints. First, source images are downsampled. Then, the gain correction coefficients of each image in a study area are accurately estimated through random cross-observation. Further, multi-view local intensity correction of each image in the study area is performed using the overlapping areas of adjacent images, and a low-resolution intensity reference map covering the study area is generated. Finally, the intensities of high-resolution source images are corrected by constructing local independent linear models of images based on the low-resolution intensity reference map. Gaofen-3 satellite images with large ranges and large time differences, covering most of China's land area, were selected to test the effect of the newly developed algorithm. After processing, the overall visual effect was good after mosaicking, the intensity transitions at the edges of the images were smooth, the intensity distributions corresponded better to actual ground objects, and the statistical result of adjacent images overlapping regions was good. This validates the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

Objectives: Radiomics is the high-throughput extraction of mineable and—possibly—reproducible quantitative imaging features from medical imaging. The aim of this work is to perform an unbiased bibliometric analysis on Radiomics 10 years after the first work became available, to highlight its status, pitfalls, and growing interest. Methods: Scopus database was used to investigate all the available English manuscripts about Radiomics. R Bibliometrix package was used for data analysis: a cumulative analysis of document categories, authors affiliations, country scientific collaborations, institution collaboration networks, keyword analysis, comprehensive of co-occurrence network, thematic map analysis, and 2021 sub-analysis of trend topics was performed. Results: A total of 5623 articles and 16,833 authors from 908 different sources have been identified. The first available document was published in March 2012, while the most recent included was released on the 31st of December 2021. China and USA were the most productive countries. Co-occurrence network analysis identified five words clusters based on top 50 authors' keywords: Radiomics, computed tomography, radiogenomics, deep learning, tomography. Trend topics analysis for 2021 showed an increased interest in artificial intelligence (n = 286), nomogram (n = 166), hepatocellular carcinoma (n = 125), COVID-19 (n = 63), and X-ray computed (n = 60). Conclusions: Our work demonstrates the importance of bibliometrics in aggregating information that otherwise would not be available in a granular analysis, detecting unknown patterns in Radiomics publications, while highlighting potential developments to ensure knowledge dissemination in the field and its future real-life applications in the clinical practice. Clinical relevance statement: This work aims to shed light on the state of the art in radiomics, which offers numerous tangible and intangible benefits, and to encourage its integration in the contemporary clinical practice for more precise imaging analysis. Key Points: • ML-based bibliometric analysis is fundamental to detect unknown pattern of data in Radiomics publications. • A raising interest in the field, the most relevant collaborations, keywords co-occurrence network, and trending topics have been investigated. • Some pitfalls still exist, including the scarce standardization and the relative lack of homogeneity across studies. [ABSTRACT FROM AUTHOR]

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Background: The application of artificial intelligence (AI) in orthodontics and orthognathic surgery has gained significant attention in recent years. However, there is a lack of bibliometric reports that analyze the academic literature in this field to identify publishing and citation trends. By conducting an analysis of the top 100 most-cited articles on AI in orthodontics and orthognathic surgery, we aim to unveil popular research topics, key authors, institutions, countries, and journals in this area. Methods: A comprehensive search was conducted in the Web of Science (WOS) electronic database to identify the top 100 most-cited articles on AI in orthodontics and orthognathic surgery. Publication and citation data were obtained and further analyzed and visualized using R Biblioshiny. The key domains of the 100 articles were also identified. Results: The top 100 most-cited articles were published between 2005 and 2022, contributed by 458 authors, with an average citation count of 22.09. South Korea emerged as the leading contributor with the highest number of publications (28) and citations (595), followed by China (16, 373), and the United States (7, 248). Notably, six South Korean authors ranked among the top 10 contributors, and three South Korean institutions were listed as the most productive. International collaborations were predominantly observed between the United States, China, and South Korea. The main domains of the articles focused on automated imaging assessment (42%), aiding diagnosis and treatment planning (34%), and the assessment of growth and development (10%). Besides, a positive correlation was observed between the testing sample size and citation counts (P = 0.010), as well as between the time of publication and citation counts (P < 0.001). Conclusions: The utilization of AI in orthodontics and orthognathic surgery has shown remarkable progress, particularly in the domains of imaging analysis, diagnosis and treatment planning, and growth and development assessment. This bibliometric analysis provides valuable insights into the top-cited articles and the trends of AI research in this field. [ABSTRACT FROM AUTHOR]

Acquisition of continuous drift ice characteristic parameters such as ice size, shape, concentration, and drift velocity are of great importance for understanding river freezing and thawing processes. This study acquired hourly oblique images captured by a shore-based camera in the winter of 2021–2022 on the Yellow River, China. The pixel point scale method for correcting oblique images is provided. The 61 lines were measured at the calibration site and the absolute error between the measured value and the calculated value was in the range of 0.009–0.850 m, with a mean error of 0.236 m. After the correction of oblique images, the true equivalent diameter of drift ice during the freezing period ranged from 0.52–13.10 m with a mean size of 3.36 m, which was larger than that of 2.30 m during the thawing period which ranged from 0.20–12.54 m. It was found that the size of drift ice increased with time during the freezing period and decreased with time during the thawing period. The fractal dimension and roundness were used to represent drift ice shape. The fractal dimension ranged from 1.0–1.3 and the roundness ranged from 0.1–1.0. A Gaussian distribution was used to estimate drift ice size and shape distributions. There is a nonlinear relationship between ice concentration and drift velocity, which can be well expressed by the logistic function. In the future, drift ice parameters for more years and hydrometeorological data for the same time need to be accumulated, which helps to analyze the freezing and thawing patterns of river ice. [ABSTRACT FROM AUTHOR]

Particle morphology is an important feature of sediments that reflects their transport history and depositional environment. In this study, we used dynamic image analysis (DIA) to measure the size and shape of beach sediments on the east and west coasts of Hainan Island in South China Sea. DIA is a fast and accurate method that can capture and analyze a large number of sediment particles in real-time. We extracted morphological descriptors of each particle, such as equivalent diameter, sphericity, aspect ratio and symmetry, and their distributions based on volume and number. We performed multivariate analysis on the particle morphological data, including alpha diversity, statistical analysis and fingerprint techniques. We found that the Shannon index, calculated by the number distribution of sediment particle morphology, can effectively discriminate between the two sites, reflecting different sediment sources, transport processes and depositional conditions. We also established a composite fingerprint based on seven morphological parameters and diversity indices, which can accurately distinguish between aeolian and hydraulic sedimentary environments. Our study demonstrates the potential application of DIA in identifying sedimentary environments and establishing sediment fingerprints. This can help us understand the sediment transport processes and depositional mechanisms in coastal areas. [ABSTRACT FROM AUTHOR]

Objectives: We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions. Methods: This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks. Results: The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. Conclusions: This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. [ABSTRACT FROM AUTHOR]

Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks. [ABSTRACT FROM AUTHOR]

Mangrove forests are important ecosystems in the coastal intertidal zone, but China's mangroves have experienced a large reduction in area from the 1950s, and the remaining mangrove forests are exhibiting increased fragmentation. A detailed mangrove dataset of China is crucial for mangrove ecosystem management and protection, but the fragmented mangrove patches are hardly mapped by medium resolution satellite imagery. To overcome these difficulties, we presented a fine-scale mangrove map for 2018 using the 2-meter resolution Gaofen-1 and Ziyuan-3 satellite imagery together with field data. We employed a hybrid method of object-based image analysis (OBIA), interpreter editing, and field surveying for mangrove mapping. The field survey route reached 9500 km, and 2650 patches were verified during the field work. Accuracy assessment by confusion matrix showed that the kappa coefficient reached 0.98, indicating a highly thematic accuracy of the mangrove dataset. Results showed the total area of mangrove forest in China for 2018 was 25,683.88 hectares, and approximately 91% of mangroves were found in the three provinces of Guangdong, Guangxi, and Hainan. About 64% of mangroves were distributed in or near the nature reserves established by national or local governments, which indicated that China's mangroves were well protected in recent years. The new fine-scale mangrove dataset was freely shared together with this paper, and it can be used by local authorities and research groups for mangrove management and ecological planning. [ABSTRACT FROM AUTHOR]

Grain shape is used less frequently in identifying the origin of sediments than grain size distributions and/or surface microtextures because of technological limitations. In addition, systematic studies on grain shape change of sand‐sized sediments from a source‐to‐sink perspective are lacking, and yet the evolutionary mechanism of grain shape along the sediment transport pathway remains poorly understood. Here, we investigated grain shape changes in sand‐sized sediments ranging from mountains to dune fields using an efficient and quantitative dynamic image analysis technique. By means of the detailed hydro‐geomorphic analysis and field sampling in a quasi‐closed sedimentary system, we collected 41 samples from the Hulunbeier Sandy Land (HSL) in northern China and its potential source region. Also, 21 typical fluvial deposit samples from different river systems in China were collected for comparison. We identify that the sphericity, symmetry, aspect ratio, and mineralogical maturity of the fluvial deposits from mountains are significantly lower than the eolian sand in dune fields. Despite the apparent inheritance of sediments from the mountains to the dune fields, the abrupt changes in the sedimentological and geochemical properties along their pathway suggest that multiple cycles of eolian abrasion within the HSL modify the sediments significantly after they are transported away from the fluvial environments. The well‐defined separation between eolian sand and fluvial deposits by grain shape parameters such as sphericity and symmetry underscores that grain shape can be a powerful tool for tracing sediment transport history in paleoenvironmental or extraterrestrial studies. Plain Language Summary: The shape, size, and surface morphology of sand grains are important for understanding the traveling story of the grain. This study focuses on the shape and geochemical composition changes of the sand grains from the Great Khingan Mountains to the Hulunbeier Sandy Land (HSL) in northern China. We measured the grain shape of each sample by capturing the images of millions of individual grains using high‐frame‐rate cameras and image analysis techniques. We found that the windblown sand is more spherical than the river sand in the mountains. This obvious difference can be used to clarify the traveling history of grains with unknown origins. The abrupt shift of both grain shape and geochemical compositions between river sand and windblown sand along the sediment traveling pathway indicates that the windblown sand in the HSL experienced a long period of wind modifications. Elongated particles and soft minerals, such as feldspars, were rounded and/or reduced in size, while more spherical and harder particles, such as quartz, were enriched in dune fields. Key Points: Repeated eolian abrasion in dune fields significantly increases dune sand's grain sphericity, symmetry, and geochemical maturityQuantitative grain shape data can be a powerful tool for tracing sediment transport history and identifying sediments of unknown originsGrain shape is difficult to differentiate the depositional environments in areas where eolian‐fluvial interactions dominate [ABSTRACT FROM AUTHOR]

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed. [ABSTRACT FROM AUTHOR]

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy. [ABSTRACT FROM AUTHOR]