Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose ...Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.展开更多
The amount of impervious surface is increasing rapidly worldwide.Although urban expansion has been studied extensively,the alteration of impervious land cover in rural regions remains under-examined.In particular,insi...The amount of impervious surface is increasing rapidly worldwide.Although urban expansion has been studied extensively,the alteration of impervious land cover in rural regions remains under-examined.In particular,insights into the utilization of these sealed surfaces are crucially needed to unravel the underlying dynamics of land use changes beyond urban areas.This study focuses on rural regions from a Swiss case study and presents an analysis of the use of sealed surfaces in such regions,rather than solely quantifying the extent of sealed surfaces.Utilizing a synergistic approach that merges detailed cadastral plans with very-high-resolution remote sensing imagery and sophisticated deep learning algorithms,we characterized the uses of sealed surfaces,including buildings and their surroundings.Our findings reveal that 2.1%of the study area’s rural regions comprises sealed surfaces-an area comparable to the sealed surfaces in the urban regions.Within these rural regions,transport infrastructure represents 68%of this impervious surface.Buildings account for 12%,and their surroundings,constituting 13%,are utilized primarily for agricultural purposes,including farming and livestock activities.The deep learning approach achieved a classification accuracy of 72%for a shallow model and 79%for a deeper model,indicating that mapping building types is possible with reasonable accuracy.The outcomes of this study underscore the critical need to factor in the presence and utilization of impervious land cover within rural regions for the sustainable management of land resources.展开更多
The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAH...The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.展开更多
When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in inco...When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.展开更多
Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,te...Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.展开更多
The Antarctic Ice Sheet harbors more than 90%of the Earth ice mass,with significant losses experienced through dynamic thinning,particularly in West Antarctica.The crucial aspect of investigating ice mass balance in h...The Antarctic Ice Sheet harbors more than 90%of the Earth ice mass,with significant losses experienced through dynamic thinning,particularly in West Antarctica.The crucial aspect of investigating ice mass balance in historical periods preceding 1990 hinges on the utilization of ice velocities derived from optical satellite images.We employed declassified satellite images and Landsat images with normalized cross correlation based image matching,adopting an adaptive combination of skills and methods to overcome challenges encountered during the mapping of historical ice velocity in West Antarctica.A basin-wide synthesis velocity map encompassing the coastal regions of most large-scale glaciers and ice shelves in West Antarctica has already been successfully generated.Our results for historical ice velocities cover over 70%of the grounding line in most of the West Antarctic basins.Through adjustments,we uncovered overestimations in ice velocity measurements over an extended period,transforming our ice velocity map into a spatially deterministic,temporally average version.Among all velocity measurements,Thwaites Glacier exhibited a notable spatial variation in the fastest ice flowline and velocity distribution.Overestimation distributions on Thwaites Glacier displayed a clear consistency with the positions of subsequent front calving events,offering insights into the instabilities of ice shelves.展开更多
Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, m...Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.展开更多
Imagery analysis is a commonly used analytical method in literary analysis.In Angela Carter’s work,the image of wolves is particularly prominent.Her“Werewolf Tetralogy”rewrites traditional culture and subverts trad...Imagery analysis is a commonly used analytical method in literary analysis.In Angela Carter’s work,the image of wolves is particularly prominent.Her“Werewolf Tetralogy”rewrites traditional culture and subverts traditional consciousness,and is the research object of many scholars.Starting from the analysis of the wolf image in The Company of Wolves,this paper uses Deleuze’s Becoming-Animal Theory to explore the construction of harmony between nature,humans and gender relations in The Company of Wolves.展开更多
The renowned masterpiece“Li Sao”by Qu Yuan contains numerous plant images for“expressing emotions and aspirations.”Exploring methods of translating plant imagery has greatly assisted in disseminating Chinese class...The renowned masterpiece“Li Sao”by Qu Yuan contains numerous plant images for“expressing emotions and aspirations.”Exploring methods of translating plant imagery has greatly assisted in disseminating Chinese classical culture and facilitating cross-cultural communication.This study conducts a comparative analysis of three translations of“Li Sao”by Xu Yuanchong,Yang Xianyi,and Hawkes,aiming to understand the different approaches to translating plant imagery and explore variations in translation effectiveness.Through data collection,comparative analysis,and case studies,this research reveals that Xu Yuanchong emphasizes free translation,Yang Xianyi tends towards literal translation,and Hawkes adopts a combination of literal,free,and phonetic translation methods.展开更多
Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led ...Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led to the deterioration of many ecosystems globally. Lake Bosomtwe, a natural Lake in Ghana and one of the six major meteoritic lakes in the world is affected by land cover changes caused by the rising effects of migration, population expansion, and urbanization, owing to the development of tourist facilities on the lakeshore. This study investigated land cover change trajectories using a post-classification comparison approach and identified the factors influencing alteration in the Lake Bosomtwe Basin. Using Landsat imagery, an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis was successfully employed to analyze the land cover change of the basin. The findings show that over the 17 years, the basin’s forest cover decreased significantly by 16.02%, indicating that population expansion significantly affects changes in land cover. Ultimately, this study will raise the awareness of stakeholders, decision-makers, policy-makers, government, and non-governmental agencies to evaluate land use development patterns, optimize land use structures, and provide a reference for the formulation of sustainable development policies to promote the sustainable development of the ecological environment.展开更多
Urban air pollution is a major challenge facing rapidly growing cities in the Middle East and North Africa (MENA) region, with vehicle emissions being a significant contributor. This study aims to analyze the spatial ...Urban air pollution is a major challenge facing rapidly growing cities in the Middle East and North Africa (MENA) region, with vehicle emissions being a significant contributor. This study aims to analyze the spatial and temporal patterns of air pollutants, particularly nitrogen dioxide (NO2), in Casablanca, Morocco, and investigate the relationship with urban development and transportation characteristics. By integrating satellite remote sensing data and Google Earth Engine (GEE) techniques, we provide a comprehensive assessment of air quality in Casablanca and demonstrate the value of using geospatial approaches for informing policymakers and urban planners. The results highlight seasonal variations in NO2 levels, the identification of pollution hotspots, and the quantification of the influence of urban features and traffic on air quality. We discuss the implications of these findings for targeted interventions to improve air quality and the potential for expanding the methodology to other pollutants and cities in the region.展开更多
Oil painting is a traditional Western painting form.With the introduction of China and the influence of China’s traditional painting and aesthetics,the painting style became more distinctive,expanding a new developme...Oil painting is a traditional Western painting form.With the introduction of China and the influence of China’s traditional painting and aesthetics,the painting style became more distinctive,expanding a new development direction of oil painting,and thus imagery oil painting came into being.Color,as the most important element in imagery oil painting,mainly plays the role of mood creation and emotional expression.Many creators are good at injecting their thoughts and emotions into the paintings through color matching,so as to enhance the artistic expression of the paintings.This paper analyzes the color expression characteristics of imagery oil painting and explores the color expression techniques in imagery oil painting and mood creation of imagery oil painting from several aspects.展开更多
The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high...The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.展开更多
By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the ...By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the author uses many literary elements to describe the characters, especially the irony and imagery. This thesis uses those rhetorical devices to vividly describe the characters and to criticize the inequality between men and women in the late 19th century.[1]展开更多
A Farewell to Arms is a classical work in the history of American literature. The author, Hemingway, creates a kind of character in his works successfully, that is, the Hemingway Hero. Due to its excellency, this nove...A Farewell to Arms is a classical work in the history of American literature. The author, Hemingway, creates a kind of character in his works successfully, that is, the Hemingway Hero. Due to its excellency, this novel is appreciated and criticized by many people. The paper aims to analyze this story in terms of structuralism. Structuralism is the belief that things cannot be understood in isolation-they have to be seen in the context of the larger structures they are part of. Parallel and contrast are two main branches in structuralism. And this novel will be analyzed in three aspects, parallel and contras in imagery, parallel and contras in characters, and parallel and contras in structure.展开更多
"Nineteen",a poem by Elizabeth Alexander,is more than a story of a gir's "first summer away from home." Through the use of carefully chosen imagery and diction,the development of characters,the..."Nineteen",a poem by Elizabeth Alexander,is more than a story of a gir's "first summer away from home." Through the use of carefully chosen imagery and diction,the development of characters,the use of details,the variation in meters and the use of enjambment,the poet has parallels both the story of the speaker and that of her lover.More importantly,the lesson the speaker learns through her experience is especially rewarding.展开更多
Compared with Spenser's Sonnet 15 in which the speaker praises his beautiful lover by comparing her to various precious things such as Saphyres,Rubies,pearles,yvory,gold and silver,the speaker in Shakespeare's...Compared with Spenser's Sonnet 15 in which the speaker praises his beautiful lover by comparing her to various precious things such as Saphyres,Rubies,pearles,yvory,gold and silver,the speaker in Shakespeare's Sonnet 130 denies that his lover is as perfect as such wonderful things as the sun,coral,snow,roses,perfume,music and goddess,and thus portrays a more human and believable beauty.In addition,he describes his lovers in four senses-the visual sense,the sense of smell,the sense of sound,and the sense of motion.As a result,he creates a vivid and genuine beauty.展开更多
基金funded by the Chongqing Normal University Startup Foundation for PhD(22XLB021)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2023B40).
文摘Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.
基金supported by the Netherlands Organization for Scientific Research NWO in the form of a VIDI grant(Grant No.VI.Vidi.198.008).
文摘The amount of impervious surface is increasing rapidly worldwide.Although urban expansion has been studied extensively,the alteration of impervious land cover in rural regions remains under-examined.In particular,insights into the utilization of these sealed surfaces are crucially needed to unravel the underlying dynamics of land use changes beyond urban areas.This study focuses on rural regions from a Swiss case study and presents an analysis of the use of sealed surfaces in such regions,rather than solely quantifying the extent of sealed surfaces.Utilizing a synergistic approach that merges detailed cadastral plans with very-high-resolution remote sensing imagery and sophisticated deep learning algorithms,we characterized the uses of sealed surfaces,including buildings and their surroundings.Our findings reveal that 2.1%of the study area’s rural regions comprises sealed surfaces-an area comparable to the sealed surfaces in the urban regions.Within these rural regions,transport infrastructure represents 68%of this impervious surface.Buildings account for 12%,and their surroundings,constituting 13%,are utilized primarily for agricultural purposes,including farming and livestock activities.The deep learning approach achieved a classification accuracy of 72%for a shallow model and 79%for a deeper model,indicating that mapping building types is possible with reasonable accuracy.The outcomes of this study underscore the critical need to factor in the presence and utilization of impervious land cover within rural regions for the sustainable management of land resources.
基金funded by National Nature Science Foundation of China,Yunnan Funda-Mental Research Projects,Special Project of Guangdong Province in Key Fields of Ordinary Colleges and Universities and Chaozhou Science and Technology Plan Project of Funder Grant Numbers 82060329,202201AT070108,2023ZDZX2038 and 202201GY01.
文摘The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.
基金This work was supported in part by the Key Project of Natural Science Research of Anhui Provincial Department of Education under Grant KJ2017A416in part by the Fund of National Sensor Network Engineering Technology Research Center(No.NSNC202103).
文摘When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.
文摘Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.
基金supported by the National Key Research and Development Program of China (Grant no.2021YFB3900105)the support from the Fundamental Research Funds for the Central Universitiesthe support by the National Key Research and Development Program of China (Grant no.2017YFA0603100).
文摘The Antarctic Ice Sheet harbors more than 90%of the Earth ice mass,with significant losses experienced through dynamic thinning,particularly in West Antarctica.The crucial aspect of investigating ice mass balance in historical periods preceding 1990 hinges on the utilization of ice velocities derived from optical satellite images.We employed declassified satellite images and Landsat images with normalized cross correlation based image matching,adopting an adaptive combination of skills and methods to overcome challenges encountered during the mapping of historical ice velocity in West Antarctica.A basin-wide synthesis velocity map encompassing the coastal regions of most large-scale glaciers and ice shelves in West Antarctica has already been successfully generated.Our results for historical ice velocities cover over 70%of the grounding line in most of the West Antarctic basins.Through adjustments,we uncovered overestimations in ice velocity measurements over an extended period,transforming our ice velocity map into a spatially deterministic,temporally average version.Among all velocity measurements,Thwaites Glacier exhibited a notable spatial variation in the fastest ice flowline and velocity distribution.Overestimation distributions on Thwaites Glacier displayed a clear consistency with the positions of subsequent front calving events,offering insights into the instabilities of ice shelves.
文摘Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.
文摘Imagery analysis is a commonly used analytical method in literary analysis.In Angela Carter’s work,the image of wolves is particularly prominent.Her“Werewolf Tetralogy”rewrites traditional culture and subverts traditional consciousness,and is the research object of many scholars.Starting from the analysis of the wolf image in The Company of Wolves,this paper uses Deleuze’s Becoming-Animal Theory to explore the construction of harmony between nature,humans and gender relations in The Company of Wolves.
基金the Innovative Training Program for College Students by Nanjing University of Aeronautics and Astronautics,which Number is:2023CX012017.
文摘The renowned masterpiece“Li Sao”by Qu Yuan contains numerous plant images for“expressing emotions and aspirations.”Exploring methods of translating plant imagery has greatly assisted in disseminating Chinese classical culture and facilitating cross-cultural communication.This study conducts a comparative analysis of three translations of“Li Sao”by Xu Yuanchong,Yang Xianyi,and Hawkes,aiming to understand the different approaches to translating plant imagery and explore variations in translation effectiveness.Through data collection,comparative analysis,and case studies,this research reveals that Xu Yuanchong emphasizes free translation,Yang Xianyi tends towards literal translation,and Hawkes adopts a combination of literal,free,and phonetic translation methods.
文摘Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led to the deterioration of many ecosystems globally. Lake Bosomtwe, a natural Lake in Ghana and one of the six major meteoritic lakes in the world is affected by land cover changes caused by the rising effects of migration, population expansion, and urbanization, owing to the development of tourist facilities on the lakeshore. This study investigated land cover change trajectories using a post-classification comparison approach and identified the factors influencing alteration in the Lake Bosomtwe Basin. Using Landsat imagery, an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis was successfully employed to analyze the land cover change of the basin. The findings show that over the 17 years, the basin’s forest cover decreased significantly by 16.02%, indicating that population expansion significantly affects changes in land cover. Ultimately, this study will raise the awareness of stakeholders, decision-makers, policy-makers, government, and non-governmental agencies to evaluate land use development patterns, optimize land use structures, and provide a reference for the formulation of sustainable development policies to promote the sustainable development of the ecological environment.
文摘Urban air pollution is a major challenge facing rapidly growing cities in the Middle East and North Africa (MENA) region, with vehicle emissions being a significant contributor. This study aims to analyze the spatial and temporal patterns of air pollutants, particularly nitrogen dioxide (NO2), in Casablanca, Morocco, and investigate the relationship with urban development and transportation characteristics. By integrating satellite remote sensing data and Google Earth Engine (GEE) techniques, we provide a comprehensive assessment of air quality in Casablanca and demonstrate the value of using geospatial approaches for informing policymakers and urban planners. The results highlight seasonal variations in NO2 levels, the identification of pollution hotspots, and the quantification of the influence of urban features and traffic on air quality. We discuss the implications of these findings for targeted interventions to improve air quality and the potential for expanding the methodology to other pollutants and cities in the region.
文摘Oil painting is a traditional Western painting form.With the introduction of China and the influence of China’s traditional painting and aesthetics,the painting style became more distinctive,expanding a new development direction of oil painting,and thus imagery oil painting came into being.Color,as the most important element in imagery oil painting,mainly plays the role of mood creation and emotional expression.Many creators are good at injecting their thoughts and emotions into the paintings through color matching,so as to enhance the artistic expression of the paintings.This paper analyzes the color expression characteristics of imagery oil painting and explores the color expression techniques in imagery oil painting and mood creation of imagery oil painting from several aspects.
基金The National Key Technologies R & D Program during the 11th Five-Year Plan Period (No.2006BAB15B01)
文摘The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.
文摘By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the author uses many literary elements to describe the characters, especially the irony and imagery. This thesis uses those rhetorical devices to vividly describe the characters and to criticize the inequality between men and women in the late 19th century.[1]
文摘A Farewell to Arms is a classical work in the history of American literature. The author, Hemingway, creates a kind of character in his works successfully, that is, the Hemingway Hero. Due to its excellency, this novel is appreciated and criticized by many people. The paper aims to analyze this story in terms of structuralism. Structuralism is the belief that things cannot be understood in isolation-they have to be seen in the context of the larger structures they are part of. Parallel and contrast are two main branches in structuralism. And this novel will be analyzed in three aspects, parallel and contras in imagery, parallel and contras in characters, and parallel and contras in structure.
文摘"Nineteen",a poem by Elizabeth Alexander,is more than a story of a gir's "first summer away from home." Through the use of carefully chosen imagery and diction,the development of characters,the use of details,the variation in meters and the use of enjambment,the poet has parallels both the story of the speaker and that of her lover.More importantly,the lesson the speaker learns through her experience is especially rewarding.
文摘Compared with Spenser's Sonnet 15 in which the speaker praises his beautiful lover by comparing her to various precious things such as Saphyres,Rubies,pearles,yvory,gold and silver,the speaker in Shakespeare's Sonnet 130 denies that his lover is as perfect as such wonderful things as the sun,coral,snow,roses,perfume,music and goddess,and thus portrays a more human and believable beauty.In addition,he describes his lovers in four senses-the visual sense,the sense of smell,the sense of sound,and the sense of motion.As a result,he creates a vivid and genuine beauty.
