Types Of Imagery Research Articles

MAPPING AREAS IMPACTED BY VOLCANIC FLOWS DURING AN ERUPTION USING SYNTHETIC APERTURE RADAR AND OPTICAL IMAGERY

Abstract. During volcanic disasters, the remoteness of the terrains combined with potentially incapacitated lifelines (e.g., disturbed transportation network) prevent ground-based surveys for timely assessment of damage extents. To that effect, we worked to combine satellite optical and Synthetic Aperture Radar (SAR) data to rapidly delineate the areas impacted by fast-moving volcanic flows during an eruption (e.g., Pyroclastic Density Currents (PDCs), lahars), which can in turn be used to target and organize the response efforts. We used the 2015 eruptions of Volcán de Colima (Mexico) and Volcán Calbuco (Chile) volcanoes to calibrate detection thresholds of different types of volcanic flows, from optical and SAR imagery. Optical imagery is used to calculate temporal changes of Normalized Difference Vegetation Index (NDVI) associated with the presence of erupted materials on the surface. SAR amplitude images are used to detect changes in surface roughness (sigma0) attributed to the emplacement of new volcanic flows. Classification of the respective NDVI and SAR amplitude signal changes for different types of volcanic flows is done using very-high-resolution imagery and ground-based data obtained during field work. Linear rescaling of minimal and maximal threshold signals is used to create probability maps of volcanic flow deposits extent, and then combined into a joint probability map to maximize the accuracy of the deposit extents. We tested our ability to generate volcanic flow extent maps during the April 2021 eruption of Soufrière St Vincent, using this detection method and the calibrated threshold values for PDCs and lahars.

An Interpretation of Light Imagery in Frankenstein from an Archetypal Perspective

In her masterpiece Frankenstein, Mary Shelley borrows and shapes many classic archetypal images and gives them personal values. Among them, the imagery of light in the form of “sun”, “moon” and “fire” appears throughout the text. This study analyzes the specific use of these three types of light imagery in this text, based on Frye’s theory of archetypal criticism. It discusses the dual characteristics of the imagery of the sun, the moon, and fire in the text, the consistency between the changes in the imagery and the emotional changes of the novel’s characters, and the foreshadowing of the plot development by the characteristics of each imagery to analyze the plot homogeneity achieved through the use of imagery and to explain the author’s style in the shape of the imagery of “light”. The interpretation of the imagery of light in Frankenstein from an archetypal perspective will help readers better understand the literary meaning of the work.

Remote sensing data from Space for road image recognition in the forestry

Paper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects in the forestry. We reviewed the principles of road detection on the optical satellite imagery. Group of direct recognition features used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples were identified: visual roads recognition, use of special software and libraries for developers, and neural networks. For the road network detection we have described methods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from the open and commercial sources, machine learning methods and neural networks. Up-to-date tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, roads seasonality. A functional summary of MapFlow plugin for road recognition in Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires and forest resources by ground means.

Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image

With the rise of GeoAI research, streetscape imagery has received extensive attention due to its comprehensive coverage, abundant information, and accessibility. However, obtaining a holistic spatial–temporal scene representation is difficult because places are often composed of multiple images from different angles, times and locations. This problem also exists in other types of geo-tagged imagery. To solve it, we propose a purely visual, robust, and reliable method for urban function identification at the street scale. We introduce a method based on a two-layer spatially dependent graph neural network structure, which handles sequential street view imagery as input (typically available in services such as Google Street View, Baidu Maps, and Mapillary), with full consideration of the spatial dependencies among road networks. In this paper, we construct an urban topological map network using OpenStreetMap data in Wuhan, China, and compute a semantic representation of the scene as a whole at the street scale using a large-scale pre-trained model. We construct the graph network with streets as nodes based on 28,693 mapping relationships constructed from 75,628 street view images and 5,458 streets. Only 5.3% of the node labels were required to obtain 10 categories of functions for all nodes in the study area. The results demonstrate that by using appropriate spatial weights, street encoder, and graph structure, our novel method achieves high accuracy of P@1 46.2%, P@3 73.0%, P@5 82.4%, and P@10 89.9%, fully demonstrating the effectiveness of the introduced approach. We also use the model to sense urban spatial–temporal renewal by computing time series street images. The model is also applicable to the prediction of other attributes, where only a small number of labels are required to obtain valid and reliable scene perception results. The example data and code is shared at: https://github.com/yemanzhongting/Knowledge-and-Topology.

Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV

LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields.

COMPARISON OF MACHINE LEARNING ALGORITHMS FOR LAND USE AND LAND COVER ANALYSIS USING GOOGLE EARTH ENGINE (CASE STUDY: WANGGU WATERSHED)

Human population growth and land use and land cover (LULC) change have always developed side by side. Considering selection of a good Machine Learning (ML) classifier algorithm is needed considering the high estimation of LULC maps based on remote sensing. This study aims to produce a LULC classification of Landsat-8 and Sentinel-2 images by comparing the accuracy performance of three ML algorithms, namely: Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). Dataset comparison ratios were also explored to find the LULC classification results with the best accuracy. Sentinel-2 is better than Landsat-8 regarding Overall Accuracy (OA) and Coefficient Kappa. The comparison ratio of the training and testing datasets with a good level of accuracy is 70:30 on both images with the average OA Landsat-8 and Sentinel-2 being 92.09% and 94.21%, respectively. The RF algorithm outperforms CART and SVM in both types of satellite imagery. The mean OA of the CART, RF, and SVM classifiers was 92.03%, 94.74%, 83.54% on Landsat-8, 93.14%, 96.15%, and 93.34% on Sentinel-2, respectively.

Characteristics and time-series monitoring by GOES-17 of volcano flume on 15 January 2022 from Tonga submarine volcanic eruption

BackgroundOn 15 January 2022, a submarine volcanic eruption occurred at Hunga Tonga. Time-series monitoring from the Geostationary Operational Environmental Satellite (GOES-17) was analysed to estimate the magnitude, location, start time, and duration of the eruption and to measure the evolving characteristics of Hunga Ha’apai Island.ResultsThe eruption starting time was between 04:10 and 04:20 UTC with an eruption intensity that increased drastically and produced a plume that reached a maximum height of about 58 km. The explosive phase lasted 13 h and consisted of multiple steam and tephra explosions with an M 5.8 earthquake. The Airmass RGB, which combines water vapor and infrared imagery from the ABI and was used to monitor the evolution of the volcano, captured a plume of gases from the eruption of Hunga Tonga volcano on 15 January 2022. This type of imagery provides information on the middle and upper levels of the troposphere and distinguishes between high- and mid-level clouds.ConclusionA sonic explosion also occurred, possibly when the volcano collapsed underwater and seawater rushed in, causing a huge displacement of seawater. The Hunga Tonga–Hunga Ha’apai eruption is not over and could worsen in the coming days. Future studies are required to assess the potential effects on stratospheric chemistry and radiation for secondary damage analysis.

The Relationship Between Music-Related Types of Synesthesia and Mental Imagery in Synesthete Musicians

Synesthesia is a perceptual and conceptual phenomenon that has been linked to a heightened capacity for mental imagery. However, our understanding of music-related types of synesthesia and mental imagery is still in its infancy. This study therefore aims to examine the relationship between music-related types of synesthesia and mental imagery in a sample of synesthete musicians. Four forms of data collection were employed: an online survey, a synesthesia test battery, an absolute pitch test, and a semi-structured interview. This article focuses on results obtained in response to open-ended questions relating to synesthete musicians’ mental imagery. An analysis of the interview data revealed heightened vividness of auditory imagery (AI), including the ability to control the speed, dynamics, and intensity of the imagery. A bidirectional analysis of the functional relationship between AI and synesthesia ascertained that multimodal mental imagery (MMI) could be triggered by AI, thus bypassing the need for sensory stimulation. Furthermore, cases of complex visual imagery (CVI) were confirmed to meet the synesthetic criteria of being automatic and involuntary, as well as being consistent and stable mappings between specific musical stimuli and CVI. These findings are noteworthy as they call into question the general assumption that synesthetic percepts are generic and simple in nature. Together, these findings contribute to our understanding of music-related types of synesthesia and mental imagery, challenge contemporary understanding of certain categorical aspects of music-related synesthetic percepts, and provide us with a deeper understanding of the nature of cross-modal associations in general.

Vividness and Use of Imagery Related to Music and Movement in People with Parkinson's: A Mixed-methods Survey Study

Parkinson's disease affects multiple aspects of movement, impacting significantly on everyday tasks. Music is used in interventions for people with Parkinson's, either to pace movements, or as an integral element of activities such as dance. This study explored self-reported vividness of two forms of imagery – motor imagery evoked by music and auditory (including musical) imagery – in people with Parkinson's, and whether and how they use these types of imagery in everyday life. Participants ( N = 199) completed: (i) vividness ratings of visual and kinesthetic music-evoked motor imagery, (ii) vividness ratings of auditory imagery, and (iii) ratings and open questions about their everyday use of these types of imagery. While most participants reported experiencing music-evoked motor imagery (with more vivid visual than kinesthetic motor imagery), <20% reported actively using music to support motor imagery in daily activities. In contrast, participants reported a broad range of contexts and uses for musical imagery (imagined music), from supporting movement (e.g., walking or exercise) to emotion regulation, and concentration. Correlational analyses associated vividness of music-evoked motor imagery with an urge to dance and musical training, while the use of musical imagery was associated with singing ability. A minority of participants reported not experiencing either motor or musical imagery, suggesting that interventions based on imagery may not be suitable for all. Nonetheless, even participants with more severe motor symptoms reported experiencing and using both types of imagery, indicating promise for their strategic use at different stages of Parkinson's. Therefore, musical and motor imagery have the potential to support rehabilitation strategies for Parkinson's, either separately or in combination.

Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities.

A new kind of sequence-to-sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG-based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery-based brain-computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi-head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.

Involuntary, Limited, and Contiguously Repeating Musical Imagery (InLaCReMI): Reconciling Theory and Data on the Musical Material Acquired by Earworms

“Earworms” have been proposed as a particular type of involuntary musical imagery (INMI) where musical material is repeated in the mind. The structure of the repetition is investigated by proposing a spreading activation model (SAM), where mental experience consists of priming and activation of nodes that represent objects, events, and relationships, including music. Music consists of chaining together nodes representing small music segments within hierarchical structures. Listening to music at a point in time activates the music's represented segment, which then primes the node representing the segment that follows. Repeating musical segments are coded recursively, with an additional layer of “context” nodes tracking global, structural location. From this basis, two hypotheses were proposed: H1 “Contiguous repetition at encoding” and H2 “Low environmental focus.” H1 predicts that when an INMI episode is a contiguously repeating segment, it must be based on music that contains contiguous repetition: it will be perceived as a subset of INMI – involuntary, limited, and contiguously repeating musical imagery (InLaCReMI). H1 challenges current views about preferred segments for looping, such as the “hook” of a tune. H2 predicts that InLaCReMI occurs when an individual is not focused on the immediate environment. In such a state there is less social imperative to activate high attentional-demand contextual information and so adherence to contextual integrity in thought is relaxed, leading to looping of recurrently activated nodes that were encoded with contiguous repetition. Additional predictions were made using SAM, demonstrating the potential for SAM to provide a unifying understanding of INMI. InLaCReMI is proposed as a frequently occurring species of INMI and confirmation of this phenomenon through more structured empirical investigation will provide novel insights into mental operation, and the nature of INMI.

Exploring the Use of Imagery to Synthesise Voice Science and Vocal Artistry When Training Singers in the Youth Choir Context

This qualitative practitioner research project seeks to broaden the debate between voice science and artistry by investigating choral conductors’ pedagogical needs. In particular, it explores the application of imagery in the vocal training and development of young singers. As the science versus imagery debate continues in the pedagogical community, the interrelationship between physiological accuracy, artistic expression and vocal health is becoming ever more prescient. Despite advances in vocal health research, relatively few studies investigate the impact of imagery use in choir training compared to other areas of voice pedagogy, and fewer still extrapolate findings to consider conductors’ professional development. This study seeks to bridge that gap. Six conducting staff members at a British youth choir participated in observations, interviews and a questionnaire aimed at identifying, analysing and evaluating their use of imagery in rehearsals. Investigation of imagery type, function and application during the training of youth choir singers begins to indicate the efficacy of various strategies in synthesising the two competing strands of science and artistry. Where imagery does not adequately unite the technical and artistic, three core themes have been identified as underlying the deficit. Findings suggest that additional training, development of physiologically accurate personalised imagery, and nuanced understanding of professional roles are all vital for ensuring the vocal health of youth choir singers.

Analisis Citraan Dalam Puisi ‘Perempuan Yang Tergusur’ Karya W.S. Rendra

This research focuses on imagery in the poetry collection “Perempuan Yang Tergusur” by W.S. Rendra. While the aim of this research is to describe the various types of imagery in the collection of poems “Perempuan Yang Tergusur” by W.S. Rendra. The method used is descriptive qualitative method. While the type of research used is library research. The research data focused on written data regarding the imagery aspect contained in the collection of poems “Perempuan Yang Tergusur” by W.S. Rendra. For data collection, the techniques used are reading techniques and note-taking techniques. The results of the analysis of the poetry of Perempuan Yang Digusur found 5 (five) forms of imagery, namely: (1) visual imagery; (2) auditory imagery; (3) olfactory imagery; (4) tactile imagery, and; (5) motion image. The imagery in the poem "“Perempuan Yang Tergusur” is described by the character "you", namely "evicted women" which is a representation of reality that occurs in marginalized communities who live in various big cities with various kinds of suffering and various injustices that they experience

An Exploratory Study on Korean National Taekwondo Competitors’ Imagery Strategy based on Their Preparation Periods

This study aimed to explore elite taekwondo competitors’ imagery strategies . The study participants were 10 elite taekwondo athletes, who worked for S business team. Data were collected through open-ended questionnaires and in-depth interviews. The data were collected based on Gould et al. (1992)’s proposed qualitative research method. The inductive content analysis of the imagery was conducted following the imagery type of Paivio (1985) and Suinn (1996). The law data and case of imagery were separated by three specialists. The results were as follows. First, elite taekwondo competitors generally used the types of imagery proposed by Paivio (1985) and Suinn (1996). In particular, imagery of anxiety regulation appeared with the highest frequency among factors and ordered imagery of motivation, imagery of skills, imagery of mental skills, and imagery of competition. Second, elite taekwondo competitors mainly used imagery of skills before two weeks for competition. They mainly used imagery of anxiety regulation the day before a competition. They used imagery of anxiety regulation and imagery of motivation on the day of a competition. They used imagery of motivation after the competition. In sum, elite taekwondo competitors used individual strategies in terms of imagery in order to ensure effective training and peak performance in competition. The strategy of imagery was applied differently based on the juncture of the competition.

IMAGERY IN JULIANNE MACLEAN’S THE COLOR OF HEAVEN

Imagery is a mental picture imagined by a reader. This research discusses imagery that existed in Julianne MacLean's novel The Color of Heaven. The Color of Heaven is a novel that tells about Sophie Duncan's life and there is a lot of imagery used in it. The objectives of this research are to find imageries in the novel, to identify and classify the types of imagery, and to reveal the purposes of the imagery. The researcher uses Laurence Perrine's theory through Johnson & Arp (2016) about seven types of imagery which are visual imagery, auditory imagery, olfactory imagery, tactile imagery, organic imagery, and kinesthetic imagery. The researcher uses qualitative research design since the analysis explains the types and the purpose of the imagery. The study uses a formalism approach because the analysis is based on the researcher's interpretation. The data are collected straight from the book by reading it several times and taking notes of the sentence that reflects imagery, then categorizing the data into its imagery types in the form of a list. The result of the analysis shows that there are found six types of imagery except one is gustatory imagery. There are 32 visual imagery, 34 auditory imagery, 12 olfactory imagery, 11 tactile imagery, 16 organic imagery, and 20 kinesthetic imagery found in the novel.

Imagery Matters: The Role of Fitness Influencers in the Reproduction of Socio-Cultural Gender Norms

Introduction This research explored Instagram ‘Fitness Influencers’, to develop an understanding of the content they post online, the level of objectification and gender differences in the extent of this objectification. The media previously has presented gendered representations of women and men, with women subjected to greater objectification than males, with similar findings in social media research. Fitness Influencers are online micro-celebrities influential on young people, thus knowledge of the images being consumed is beneficial due to the associated detrimental effects of unconsciously internalising objectified media and that promoting unattainable beauty standards. Methods A content analysis of 90 influencer images was conducted to provide an understanding of the type of images shared, followed by a Chi square (χ2) to determine gender differences; the qualitative content analysis phase identified four main themes, lifestyle, brand endorsements, engaged in activity and objectification. Results There were no significant gender differences found (p>0.05) however, objectification sub-themes found significant differences in muscularity between males and females and in sultry poses (p<0.001), in line with gender stereotypes and norms in society. Conclusion The identification of greater objectification of males highlights the need for greater consideration for male’s body image, and greater consideration for the content being shared online more generally, due to the associated detrimental effects of consuming certain types of imagery.

Использование данных дистанционного зондирования Земли из космоса для распознавания изображения дорог в лесном хозяйстве

Paper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects. We have characterized principles of road detection on the imagery. A group of direct deciphering signs used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples identified: visual roads recognition, use of special software and libraries for developers, and use of neural networks. For the road network detection we have described methods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from the open and commercial sources, machine learning methods and neural networks. Actual tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, seasonality of roads use. A functionality summary of MapFlow plugin for road recognition in Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires and forest resources by ground means.

The Effect of Imagery Ability on Mental Toughness and Sportive Confidence Level in Athletes

The aim of this study was to determine the effect of the imagery abilities of the athletes on their mental toughness and sportive confidence levels. 627 individual and team sports athletes that were active sports and licensed participated in the study in the Eastern Anatolia region of Turkey. In the study, the sports imagery scale developed by Hall et al., (1998) adapted to Turkish by Kızıldağ (2007) was used with the aim of determining the types of imagery of athletes, Mental toughness scale in sports developed by Sheard et al. (2009) adapted to Turkish by Altıntaş and Koruç (2016) was used to determine the level of mental toughness in the sports events and Sportive continuous self-confidence scale developed by Vealey (1986) and adapted to Turkish by Yıldırım (2013) was used to determine athletes' sportive confidence. In collecting data, face to face survey method was preferred. The Pearson Correlation and Linear Regression analysis were used in the analysis of the data obtained through the SPSS package program. According to the results of the analysis, it was found that there were significant relationships between the imagery ability in athletes and the lower dimensions of mental toughness and sportive confidence. According to this result, it was shown that the ability of imagery in athletes is an important factor in their mental toughness and sportive confidence levels.

A novel Deep Capsule Neural Network for Vowel Imagery patterns from EEG signals

Speech imagery has recently been included in the design of Brain–Computer Interfaces to develop novel communication or control systems based on brain activity that does not need external stimulation like evoked potentials. Three types of speech imagery exist: imagining words, syllables, or vowels. Words are composed of syllables and syllables by consonants and vowels. However, imagining just vowels generates Speech-Related Potentials that reduce the complexity of the brain activity in Electroencephalographic signals. This paper proposes a new classifier method for communication or control purposes based on a novel Deep Capsule Neural Network for Vowel Imagery recognition. The method is named Capsules for Vowel Imagery (CapsVI). CapsVI has the appropriate number and size of convolution kernels to find the relevant features of the input. The size of the capsules is estimated based on the feature patterns found during the training. The class capsules were developed based on prototypes patterns of /a/, /u/, and/no vowel/ classes. The experiments were developed with the DaSalla dataset. Results indicate that capsules model the Speech-Related Potentials of Vowel Imagery correctly enough to generate the necessary information for vowel pairwise classification. Furthermore, the results also demonstrate that CapsVI recognizes vowels with an average accuracy of 93.32% on the pairwise classification, and the best precision by subject is 94.68%. These results are the best in Vowel Imagery recognition of the English language reported in the literature.

Imagery of Intrinsic Elements Found in Edgar Allan Poe’s Poetry

This research was purposed to found and explained the kinds of imagery in three poetries by Edgar Allan Poe. All of content poetry is true story of Edgar Allan Poe’s life. The poetry that was taken by researchers are “To My Mother, Serenade, and Spirits of the Dead”. The researcher analyzed seven types of imagery as follows: visual imagery, auditory imagery, olfactory imagery, gustatory imagery, tactile imagery, organic imagery, and kinesthetic imagery. The method was regarded qualitative as research design. The researcher used documentation technique in collecting the data. Analysis data in this research implied content analysis approach. The result of this research showed forty-seven imageries in the three poetries of Edgar Allan Poe. The imagery found as follows;23 visual, 4 auditory, 2 olfactory, 2 tactile, and 16 organics. The researcher found five types of imagery in three poetries, but the visual imagery is very dominant raised of each poetry.