Types Of Imagery Research Articles

Comparative use of UAV and Satellite Images in discrimination and estimation of cashew plantation areas

Cashew plantations generate significant interest in Benin due to their high socioeconomic value for the population. A thorough understanding of the spatial distribution of these plantations is crucial for comprehending their environmental and socioeconomic impacts. In this study, various types of multi-sensor imagery were compared to assess each sensor's capabilities in mapping plantation areas. The study was conducted in the Savè commune, a major industrial cashew-producing region. Multispectral sensors from Landsat-8 Operational Land Imager (OLI), Sentinel-2A, and UAV multispectral platforms, along with ground surveys, were fused and classified using the Random Forest algorithm. The study results allowed for the assessment of uncertainties associated with different platforms in detecting cashew plantations in the test area. Classification using Random Forest algorithms on UAV, Sentinel, and Landsat platform images yielded overall accuracies of 83%, 65%, and 48%, respectively. Producer and user accuracies were 94% and 75% for the UAV platform, 98% and 71% for the Sentinel platform, and 91% and 77% for the Landsat platform in cashew tree detection. This study demonstrates the complementarity among various platforms in detecting and mapping cashew plantations.

Barokowe „wojny” (z) ciałem

The article presents the topic of the allegorical war against the body, which in the post-Tridentine culture was perceived in an ambivalent way: as sinful and evil and as good because it was the work of God. Allegorical battles were also presented, in which the body is a tool in battle. Attention was paid to the sources of this ambivalence – the dualistic nature of man. The different needs of the soul and body have the nature of a conflict. The fight for man is fought by: Amor Divinus and Amor profanus. The bodies of people who lived piously and wanted to achieve mystical union with Christ are shown in the laudatory theme – similarly to. The bodies of sinners who succumbed to sensual temptations during their lives are depreciated, despised, presented in turpistic aesthetics, and ultimately condemned to the torments of hell. The ancient sources of this type of imagery were indicated – the ideas of Plato, the thought of Seneca and Horace – and the affiliations between ancient culture and post-Tridentine poetry. It has been established that the Body of Christ has a unique status. Descriptions of the tormented Jesus, permeated with brutal naturalism, were intended to provoke reflection and influence the conversion of sinners. The Judeo-Christian tradition and the heritage of Greco-Roman antiquity were transformed and adapted to the post-Tridentine culture in the 17th century.

Sensory representations in primary visual cortex are not sufficient for subjective imagery

The contemporary definition of mental imagery is characterized by two aspects: a sensory representation that resembles, but does not result from, perception, and an associated subjective experience. Neuroimaging demonstrated imagery-related sensory representations in primary visual cortex (V1) that show striking parallels to perception. However, it remains unclear whether these representations always reflect subjective experience or if they can be dissociated from it. We addressed this question by comparing sensory representations and subjective imagery among visualizers and aphantasics, the latter with an impaired ability to experience imagery. Importantly, to test for the presence of sensory representations independently of the ability to generate imagery on demand, we examined both spontaneous and voluntary imagery forms. Using multivariate fMRI, we tested for decodable sensory representations in V1 and subjective visual imagery reports that occurred either spontaneously (during passive listening of evocative sounds) or in response to the instruction to voluntarily generate imagery of the sound content (always while blindfolded inside the scanner). Among aphantasics, V1 decoding of sound content was at chance during voluntary imagery, and lower than in visualizers, but it succeeded during passive listening, despite them reporting no imagery. In contrast, in visualizers, decoding accuracy in V1 was greater in voluntary than spontaneous imagery (while being positively associated with the reported vividness of both imagery types). Finally, for both conditions, decoding in precuneus was successful in visualizers but at chance for aphantasics. Together, our findings show that V1 representations can be dissociated from subjective imagery, while implicating a key role of precuneus in the latter.

Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.

Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.

Integrating thermal and optical models with legacy excavation data for spatial inventories

Researching at previously excavated archaeological sites comes with its own challenge set, which includes ground-truthing previously unearthed architectural features to correlate old and new trenches. This study presents a comprehensive examination technique to approach architectural features at mound-type archaeological settlements through the synergistic utilization of thermal and optical imagery obtained via remote sensing technologies. Employing an interdisciplinary approach involving various types of imagery (optical, thermal, and radar) embedded in archaeological data, this research aims to reevaluate legacy spatial data and enhance the understanding of subsurface archaeological features by exploiting the complementary advantages of both thermal and optical sensors.This study involves the acquisition, processing, and integration of Unmanned Aerial Vehicle (UAV)-based thermal and optical datasets to derive a complete representation of archaeological landscapes. Ground-truthing and validation exercises are incorporated to ensure the accuracy and reliability of the results. The results contribute to advancing the field of archaeological remote sensing and highlight the significance of integrating diverse sensing modalities for a comprehensive and nuanced interpretation of archaeological landscapes. The techniques exemplified with two mound-type case sites help to generate high-resolution mappings which provide tools to increase the accuracy of on-site legacy data and help digital protection of the sites by generating digital twins. This presents a less costly method than conventional photogrammetric techniques, making it feasible and advantageous for field archaeologists or researchers dealing with legacy spatial data.

Deep learning-based detection of qanat underground water distribution systems using HEXAGON spy satellite imagery

Qanats are a remarkable type of ancient hydraulic structure for sustainable water distribution in arid environments that use subterranean channels to transport water from highland or mountainous areas. The presence of the qanat system is marked by a line of regularly spaced shafts visible from the surface, which can be used to detect qanats using satellite imagery. Typically, qanats have been documented by field mapping or manual digitisation within a Geographic Information System (GIS) environment. This process is time-consuming due to the numerous shafts within each qanat line. However, several automated methods for detecting qanat structures have been explored, using techniques such as morphological filters, custom convolutional neural networks (CNN) and, more recently, YOLOv5 and Mask R-CNN. These approaches used high-resolution RGB images and CORONA images. However, the use of black and white CORONA in CNNs has been limited in its applicability due to a high rate of false positives.This paper explores the potential of YOLOv9 in processing the black and white HEXAGON (KH-9) high-resolution spy satellite system launched in 1971. Two areas in Afghanistan (Maiwand) and Iran (Gorgan Plain) were selected to train the system images extracted from HEXAGON imagery and artificial synthetic data. The training dataset was augmented using the Albumentation library, which increased the number of tiles used. The model was tested using two types of HEXAGON imagery for selected areas in Afghanistan (Maiwand), Iran (Gorgan Plain) and Morocco (Rissani), and CORONA imagery in Iran (Gorgan Plain).Our study provided a model capable of predicting the location of qanat shafts with a precision of over 0.881 and a recall of 0.627 for most of the case studies tested. This is the first case study aimed at detecting qanats in different landscapes using different types of satellite imagery. Using real, augmented, and artificial data allowed us to generalise the representation of qanats into lineal groups of circular features. Thanks to applying labelling for individual qanats and their pairs as separate classes, our approach eliminated most of the isolated and clustered false positives.

GENRE-STYLE AND SPIRITUAL-ETHICAL METAMORPHOSIS OF FRENCH MUSICAL THEATER OF THE 17TH-20TH CENTURIES

The purpose of the work is to identify the genre-stylistic and spiritual-meaning specifics of the French musical theater of the New Age and its evolutionary paths in the European culture of the 17th-20th centuries. The historical paths of the development of French musical theater of the 17th–20th centuries reveal its genre and stylistic diversity, represented by the typologies of “lyrical tragedy”, “salvation opera”, Empire opera, “grand” French opera, lyric opera and numerous varieties of samples of experimental musical theater and synthetic genres formations of the 20th century. The timbre-intonational and dramaturgical palette of the mentioned genres is organically inscribed in the stylistic priorities of French culture and music of the New Age Baroque, Classicism, Empire, Romanticism, Biedermeier, etc. At the same time, the mentioned typologies of the French musical theater combine a number of common features, among which we single out the essential role of its cult-mysterious primary basis, the corresponding types of imagery and their leading features, oriented to the cultivation of Christian virtues, the idea of spiritual transformation, the harmonious union of the earthly and sacred worlds. The appeal to educational and didactic, as well as spiritual and ethical factors in French opera (even at the level of biblical thinking of own history) conditions its appeal to large-scale bright spectacular performances with a large number of mass and ballet scenes, the genesis of which went back to the spiritual traditions of the culture of French absolutism in its historical development The specified genre metamorphoses of modern French opera at the level of “genre memory” focused the attention of the listener-spectator first of all on the original manner of conveying the poetic Word, its spiritual and ethical meaning, which went back to the “mysterious mode” of the French musical theater, on a clear fixation of dance-plastic movement-gesture, which collectively retained its significance at all stages of its historical existence.

Faster R–CNN, RetinaNet and Single Shot Detector in different ResNet backbones for marine vessel detection using cross polarization C-band SAR imagery

Detection of marine vessels plays an important role in monitoring, managing and securing seas and oceans, and forms the foundation of Maritime Domain Awareness (MDA). Although marine vessel detection has remained an active area of research for many years, unlike other object detectors, techniques of detection have been left far behind and lack systematic robustness. Hence, this study compared the performance of Faster R–CNN, RetinaNet and Single Shot Detector (SSD) across different epochs and complexities of ResNet architectures using Sentinel-1 VH polarization in one of the busiest ports in the Philippines. In particular, the models were created from the training samples dataset derived from Sentinel-1 VH imagery captured on January 12, 2024 in ResNet-34, -50, and −101 backbones, and 20 and 100 epochs. In this study, a total of 18 different object detector models were created for the comparative analysis. The models were tested with respect to different dates but having the same imagery type to determine their applicability across other base maps. Faster R–CNN with the highest F1 score of 0.85 outperformed RetinaNet with a highest F1 score of 0.74 and SSD with the highest F1 score of 0.38. The fastest model created was SSD, with an average speed of 9 to 44 minutes, followed by RetinaNet with an average speed of 8 to 58 minutes; the slowest is Faster R–CNN with an average speed of 25 minutes to 1 hour and 3 minutes. The use of Sentinel-1 VH imagery for marine vessel detection is a viable alternative, but the choice of object detectors should be carefully considered. The presence of geospatial software with advance deep learning tools improves remote sensing applications and allows non-programmers to optimize their competence. This study highlights the potential utilization of other imagery with higher spatial resolution, testing of other deep learning algorithms, finetuning of parameters, and utilization of higher computing infrastructure. The findings of this study can be applied in other areas for MDA, particularly in regions where advanced remote sensing applications have yet to be extensively explored.

The Study of Stylistics in the Novel Phosphorus, the Man Who Finds the Most Beautiful Place of Retno Mawarni’s Work

The aim of the research is to analyze the use of language styles in literature. The research method uses descriptive methods. The data source used is the novel Phosphor, Men Who Look for the Most Beautiful Places by Retno Mawarni, published by Sabil Yogyakarta in July 2013, with 370 pages. Based on the research results, it is known that there are eight types of imaging available; All types of imagery are used by the author in the novel Phosphorus, the man who found the most beautiful place by Retno Mawarni, namely visual imagery, auditory imagery, olfactory imagery, taste imagery, thought imagery, motion imagery, tactile imagery, and feeling imagery. Apart from imagery, a stylistic study based on rhetorical elements, namely the language style in the phosphorus novel, The Man Who Found the Most Beautiful Place by Retno Mawarni, shows that the comparative language style (figure of speech) used is the simile style (smile), the personification language style, and the allegorical language style. Contradictory language styles are hyperbole, language style, and irony. The overall style of language used by the author aims to increase the effect of the speech. The things that the author can put forward are: (1) for teaching literature in the results, additional teaching materials should be used in implementing Indonesian language and literature learning in schools; (2) for fans and lovers of literature, research results should be used as additional information as well as a reference in conducting literary research, especially regarding stylistics.

Is there also a hidden health cue in the background? The impact of natural imagery on product attitude in food marketing

Prior studies have primarily concentrated on the role of food imagery, which depicts the food itself or its ingredients, in conveying food messages, with less emphasis on background imagery. Background imagery, which often carries symbolic or metaphorical meanings through the depiction of food-related and non-food-related subjects, also serves as a vital medium for communicating food messages. This study investigates the role of natural imagery, a type of background imagery, in transmitting health-related attributes of food. This study employed an implicit association test (IAT) alongside three scenario experiments. The findings demonstrate that natural imagery triggers a health association with food, leading to a favorable product attitude, referred to as the “health metaphor effect.” Subsequently, the boundary conditions for this effect were examined, revealing its presence in healthy product categories as opposed to unhealthy ones. Building upon these findings, the study also assessed the influence of food imagery in food visuals on the role of natural imagery. The result indicates that, for healthy foods, the health metaphor effect is more pronounced when the food imagery is depicted in a processed (vs. raw) state.

Motor Intentions Decoded from fMRI Signals.

Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

Between Wine and Tea: A Discussion Based on Master Taixu’s Use of Dual Imagery

The imagery of wine and tea is important in classical and modern Chinese poetry, with an intricate relationship between the two especially evident in the work of Taixu 太虛 (1890–1947), a prominent poet–monk in 20th-century China. Taixu’s attitude toward wine—a drink that is deeply rooted in Chinese culture—evolves significantly over time, from initial approval to eventual condemnation due to its detrimental effects on both personal health and society. Nevertheless, it continues to feature prominently in his poetry. The same is true of tea, which Taixu often uses to evoke either Buddhist study or his own healthy lifestyle. This article explores this and other complex meanings in Taixu’s poems, such as his association of wine with knights and tea with hermits. It also discusses how he achieves a delicate balance between the two beverages, sometimes employing both types of imagery in a single poem—a literary innovation that helped to establish his reputation as a central figure in modern Chinese poetry.

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.

Mental Imagery and Social Phobia: What Form of Mental Imagery – Based Cognitive Behavioral Therapy is Most Effective?

Mental imagery is the internal experience of representing sensory information that is normally perceived through the senses, entirely in the mind. Visual mental imagery, which is the most studied type of imagery, is commonly referred to as “visualization,” or “seeing through the mind’s eye.” One of the defining features of imagery is that it does not require an external stimulus for the creation of a mental image. Imagery can also be either voluntary or involuntary. Involuntary mental imagery is important as it is the essential element of many psychological disorders, such as post-traumatic stress disorder, which is characterized by intrusive “flashbacks” of traumatizing memories in the form of imagery. Mental imagery has general significance in our lives as it is often used as a tool for remembering information, and it is increasingly being used in clinical fields both to study and to help treat mental health disorders. The purpose of this paper is to review literature on the neuroscience of mental imagery, emotion, and its clinical applications. This paper will also discuss different forms of cognitive-behavioral therapy (CBT) and their effectiveness in treating anxiety and mental health disorders, specifically social phobia. Additionally, this paper will attempt to fill in the gap in previous studies by providing insight into which form of CBT may be most effective for treating social phobia.

The picture is in the collection “The Suggestion of the Wounded and the Injury of the Wounded” by Al-Husri Al-Qayrawani

The picture is in the collection “The Suggestion of the Wounded and the Injury of the Wounded” by Al-Husri Al-Qayrawani

More developed means dangerous: spatial evidence of multi-decadal urbanising watershed and its impact to flash flood in metropolitan Semarang-Indonesia

ABSTRACT This research conducts spatial verification of the watershed urbanisation phenomenon’s occurrence, which has implications for environmental problems and disasters. The evaluation employs a spatial analysis tool that incorporates variables such as changes in land cover, water run-off, flood risk, and stakeholders’ participation in watershed spatial planning. The study is supported by spatio-temporal data from remote sensing from 1970 to 2023, utilising various types of Landsat satellite imagery, including Landsat 5 TM, 7 ETM, and 8 OLI. The data is processed using GIS and several calculations assessing the impact of land cover changes on run-off discharge. The findings reveal significant differences in land cover in the upper area of the Garang Watershed-Metropolitan Semarang, Indonesia. This situation is exacerbated by overlapping interests in management, resulting in a watershed spatial plan that does not support land conservation efforts and prioritises the growth of upper-class settlements in the watershed area. These results demonstrate that the spatial policy in the Garang Watershed has yet to fully accommodate the interests of sustainable development, particularly in addressing the issue of watershed urbanisation.

Satellite-Derived Bathymetry in Support of Maritime Archaeological Research—VENμS Imagery of Caesarea Maritima, Israel, as a Case Study

Deriving bathymetry by means of multispectral satellite imagery proves to be a replicable method, offering high-resolution coverage over large areas while keeping costs low. Maritime archaeologists often require bathymetric mapping at a high resolution and with a large spatial coverage. In this paper, we demonstrate the implementation of SDB in maritime archaeology using high-resolution (5 m/pixel) data from Vegetation and Environment monitoring on a New Micro-Satellite (VENμS) imagery. We focus on the area of the Roman harbour of Sebastos, located at Caesarea Maritima along the Israeli coast of the Eastern Mediterranean. For extracting SDB, we take an empirical approach, which is based on the integration of satellite imagery and sonar depth measurements, resulting in a blue-green band ratio algorithm that provides reliable results up to a water depth of 17 m. Comparison with in situ depth measurements yielded an RMSE of 0.688 m. The SDB mapping is complemented by satellite-based identification of above- and below-water rocks. The presented approach can readily be replicated in other regions using various types of multispectral satellite imagery, particularly when only coarse bathymetric sonar data are available, thus substantially contributing to our ability to perform maritime archaeological research.

Psychophysiological strategies for enhancing performance through imagery–skin conductance level analysis in guided vs. self-produced imagery

Athletes need to achieve their optimal level of arousal for peak performance. Visualization or mental rehearsal (i.e., Imagery) often helps to obtain an appropriate level of activation, which can be detected by monitoring Skin Conductance Level (SCL). However, different types of imagery could elicit different amount of physiological arousal. Therefore, this study aims: (1) to investigate differences in SCL associated with two instructional modalities of imagery (guided vs. self-produced) and six different scripts; (2) to check if SCL could differentiate respondents according to their sport expertise. Thirty participants, aged between 14 and 42 years (M = 22.93; SD = 5.24), with different sport levels took part in the study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task, SCL was monitored. We analysed the mean value, variance, slope and number of fluctuations per minute of the electrodermal signal. Unsupervised machine learning models were used for measuring the resemblance of the signal. The Wilcoxon signed-rank test was used for distinguishing guided and self-produced imagery, and The Mann–Whitney U test was used for distinguishing results of different level athletes. We discovered that among others, self-produced imagery generates lower SCL, higher variance, and a higher number of fluctuations compared to guided imagery. Moreover, we found similarities of the SCL signal among the groups of athletes (i.e. expertise level). From a practical point of view, our findings suggest that different imagery instructional modalities can be implemented for specific purposes of mental preparation.

Metaphoric veiled image-schema of kinship organization in ceremonial space: A south Andean case

This study is an interdisciplinary approach to a veiled metaphoric design expressed in the present-day spatial layout of ecologically-derived patronyms of Mapuche lineages and families positioned in public ceremonial plazas. The perspective combines ethnoarchaeological, cognitive, iconographic, oral tradition, allegoric metaphor, and historical approaches to the organization and meaning of this design, taking into account the past and present dimensions of ritual media (e.g., political, ideological and sensorial) to render a concept publicly graspable. Shamans and other informants state that the design represents an ancient foundational schema established for intercommunity political solidarity during times of both peace and conflict in the Spanish colonial and Republican era of the south-central Andes of Chile. Shamans were asked to draw their mental image of the hidden design of the plaza to pictographically reveal its visual representation. By examining the iconography of patronyms depicting a loosely structured order of families hierarchically positioned in ceremonial space, the invisible nature of this pattern and its wider political and kinship meaning is considered. Viewed from an archaeological perspective, this schema is suggested to be associated with a “chiefdom” or intermediate, polity-level society, and may represent a type of mental imagery and template that served as a precursor to the visible iconography on wood, stone, adobe, ceramic, textile and other media of complex Pre-Colombian societies. Ultimately, this schema is a conceptual metaphor: mapping and structuring knowledge of a trophic hierarchy of elements in the natural world to evoke a political and public organizational principle through sensorial experiences and life concerns in the invisible and visible domains of an Andean-like ceremonial format.

Using time-series imagery and 3DLSTM model to classify individual tree species

ABSTRACT Classification of individual tree species (ITS) is critical for fine-scale forest surveys. However, it is difficult to obtain the complete and high-precision data needed for ITS classification in large areas. Lower spatial resolution time-series imagery is more accessible than other types of imagery and contains rich phenological information. In this study, after delineating individual tree crowns using a 0.2-m unmanned aerial vehicle (UAV) image, we used 3-m time-series imagery and a new 3DLSTM model to identify ITS at the Gaofeng Forest Farm in Guangxi Province, China. The 3DLSTM ITS classification model combines three-dimensional convolutional neural network (3D CNN) and long short-term memory (LSTM) models; thus, spatial, multiband, and time-series information can be extracted simultaneously to identify ITS more accurately. In this study, when only 3-m Planet time-series imagery was used for classification, the 3DLSTM model offered an ITS classification accuracy of 92.68%, outperforming two ITS classifiers (DenseNet or AlexNet model) based on one individual image. Moreover, the 3DLSTM model was better at identifying broad-leaved tree species than other deep-learning models. The experimental results proved that ITS classification could be significantly improved using only 3DLSTM and time-series images, offering the possibility of classifying large-scale ITS at a low cost.