Spatial Imagery Research Articles

Spatial information-enhanced hyperspectral imagery classification based on joint spatial-aware collaborative representation

To address the insufficiency of texture information-based classification features to classify samples, we proposed two methods for spatial information-enhanced hyperspectral imagery classification based on joint spatial-aware collaborative representation (JSaCR). First, we introduce a texture regularized-based joint spatial-aware collaborative representation (TRJSaCR) method, in which prior texture is regarded as a regularization term to constrain the coefficient of the objection function of JSaCR and the closed-form solution is obtained to reconstruct the test sample. Second is a spatial information-assisted discrimination rules (SIDR) method coupled with TRJSaCR (TRJSaCR-SIDR) for classification. More precisely, the label information of the test samples and their corresponding neighborhoods are specified by TRJSaCR-SIDR, and the final labels are determined by considering their neighborhood label distribution. Our work aims to broaden the knowledge of the utilization of spatial information in hyperspectral classification. Experimental results on two benchmark hyperspectral datasets, Indian Pines and Pavia University, indicate that the proposed algorithms are superior to other state-of-the-art classifiers.

EEG to Identify Attempted Movement in Unresponsive Wakefulness Syndrome.

Assessment of consciousness following severe brain-injury is challenging. Our hypothesis is that electroencephalography (EEG) can provide information on awareness, in terms of oscillatory activity and network task-related modifications, in people with disorders of consciousness. Similar results were obtained with neuroimaging techniques; we aim at demonstrating the use of EEG, which is low cost and routinely implemented, to the same goal. Nineteen-channel EEG was recorded in 7 persons with unresponsive wakefulness syndrome (UWS) and in 10 healthy subjects during the execution of active (attempted movement) and passive motor tasks as well as 2 mental imagery tasks. Event-related synchronization/desynchronization (ERS/ERD), coherence and network parameters were calculated in delta (1-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-12 Hz), and beta (13-30 Hz) ranges. In UWS subjects, passive movement induced a weak alpha2 ERD over contralateral sensorimotor area. During motor imagery, ERD was detected over the frontal and motor contralateral brain areas; during spatial imagery, ERS in lower alpha band over the right temporo-parietal regions was missing. In UWS, functional connectivity provided evidence of network disruption and isolation of the motor areas, which cannot dialog with adjacent network nodes, likely suggesting a diffuse structural alteration. Our findings suggest that people with a clinical diagnosis of UWS were able to modulate their brain activity when prompted to perform movement tasks and thus suggest EEG as a potential tool to support diagnosis of disorders of consciousness.

Shaping our personal past: Assessing the phenomenology of autobiographical memory and its association with object and spatial imagery.

A new instrument has been developed that allows a comprehensive assessment of the relevant dimensions of the phenomenology of autobiographical memories (Assessment of the Phenomenology of Autobiographical Memory, APAM), and their association with visual object and spatial imagery has been examined. An initial version of APAM consisting of 30 items (the first 28 measured on a seven-point Likert-type scale) was developed and administered to a sample of 138 undergraduates. To test whether each item consistently measured the same dimension across different memories, all questions were rated for 12 cues. Results showed that 25 Likert-type items possessed adequate levels of internal consistency and unidimensionality across cues. We also found that higher levels of visual object imagery were associated with more sensory details and recollective qualities of memory, and with stronger experience of sensory and emotional reliving. The theoretical and practical usefulness of APAM as well as the relevance of visual object imagery in the phenomenology of autobiographical memory are discussed.

Mapping multiple tree species classes using a hierarchical procedure with optimized node variables and thresholds based on high spatial resolution satellite data

ABSTRACT Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively.

The enhancement of visuospatial abilities through pencil-and-paper or computer-aided training: What is more effective in 7-year-old pupils?

ABSTRACT This investigation examines the impact of two psychoeducational programs (i.e., computer-assisted versus pencil-and-paper) for the empowerment of visuospatial skills of 7-year-old pupils. At pretest, posttest and at a three-month follow-up, 58 children attending the second grade of Italian primary school completed a battery of tests assessing visuospatial problem solving, speed of processing, spatial comprehension, mental imagery, passive and active verbal working memory, respectively. After the pretest, 19 participants were presented the computer-assisted “Recovery in … visuo-spatial abilities” training, whereas further 20 pupils were trained with the pencil-and-paper “Recovery in … visuo-spatial abilities” psychoeducational intervention, respectively. At posttest and follow-up, the positive impact of the computer-assisted training has been documented, whereas the efficacy of the pencil-and-paper intervention has been found only at the posttest. Overall, the computer-based program was particularly effective in favoring the development of spatial comprehension, mental imagery, and processing speed in second graders.

The image of memory: relationship between autobiographical memory and mental imagery in Korsakoff syndrome

We evaluated the relationship between autobiographical memory and mental imagery in Korsakoff syndrome (KS). We invited patients with KS and control participants to retrieve personal events and to perform measures of visual imagery (Taller/Wilder task), and spatial imagery (Clock Angles task). On the Taller/Wilder task, participants had to generate a mental image of an object presented as a word and determine whether the object was taller than it was wide. On the Clock Angles task, participants had to imagine the angle created by the hands on a clock set to a certain time and determine whether that angle was <90°. Analyses showed impaired spatial imagery and autobiographical memory but intact visual imagery in KS. Analyses also demonstrated significant correlations between visual imagery, spatial imagery and autobiographical memory in patients with KS and control participants. Our findings demonstrate that autobiographical retrieval in KS is associated with the ability to retrieve (visual imagery) and manipulate mental images (spatial imagery).

Movement, Space, and Power in Cynewulf’s Juliana

ABSTRACT Cynewulf’s Juliana has long been the subject of feminist criticism, particularly focusing on the moments when Juliana claims power and agency in enclosure, even in her imprisonment. This article reevaluates Juliana’s claims to power and agency from the perspective of modern critical theory and pragmatics, arguing that Juliana uses place-deixis to claim her body as a sovereign space, challenging the secular and political authority imposed upon her by her pagan father, her fiancé, and the emperor. These authorities force Juliana to move physically — first from her household, then to court, and then to different locations where she is tortured and imprisoned. However, her bodily movement contrasts not only with her spiritual fixity, but with her continuing sovereignty over the space of her body. Place-deixis and spatial imagery work with references to God’s all-encompassing physical presence to redefine “power the relationship” and dwarf the limited authority that earthly authorities seem to wield.

Spatial Analysis of Livestock Grazing and Forest Service Management in the High Uintas Wilderness, Utah

This case study addresses the Forest Service reauthorization for grazing of domestic sheep in Utah’s High Uintas Wilderness, USA. It provides an approach using spatial analysis and aerial imagery to evaluate the lands capable of being grazed based on Forest Service criteria and field surveys. The resulting model and analysis demonstrated that the Forest Service has not applied its own criteria. This has led to the Forest Service overestimating the amount of land and numbers of sheep that can be supported in the study area. Past field studies show this has resulted in environmental damage by grazing sheep. Our analysis concludes that the numbers of domestic sheep should be greatly reduced to protect these lands and wilderness values. Limitations of the study include the lack of a suitably detailed soil survey to determine erosion susceptibility, a lack of ground cover data, a lack of Forest Service data for the level of grazing use, or utilization, and the lack of a Forest Service quantitative measurement of vegetation production in each plant community and soil type. In the end, our use of aerial imagery, GIS determinations of areas of steep slopes and dense forests, and our measurement of vegetation production in the dominant soil types showed most of the land is not capable for grazing domestic sheep even in the absence of this other data.

The Two Eyes of the Blind Mind: Object vs. Spatial Aphantasia?

Individual variability in imagery experiences has long attracted the interest of philosophers, educators, and psychologists. Since Aristotle’s time, it was assumed that imagery is a universal ability, so everyone possesses it. Galton first measured the vividness of subjective imagery experiences, and discovered that some individuals reported zero imagination. Recent research has coined the term “aphantasia” — an inability to form mental imagery, or having a “blind mind’s eye” (Zeman, Dewar, &amp; Della Sala, 2015). We argue that there may be more than one type of aphantasia. Substantial behavioral and neuropsychological evidence has demonstrated a distinction between visual-object imagery (mental visualization of pictorial properties such as color, shape, brightness, and texture) and visual-spatial imagery (mental visualization of spatial locations, relations, and transformations). Notably, visual imagery is not a unitary ability, so individuals who excel in object imagery do not necessarily excel in spatial imagery, and vice versa. Here we argue that the commonly described “aphantasia” is not a general imagery deficit but rather a visual-object deficit of imagery (as aphantasic people are often identified by low scores on the Vividness of Visual Imagery Questionnaire, which assesses object imagery only). We hypothesize that “spatial aphantasia” (the inability to imagine spatial properties and relationships) can be a separate type of imagery deficit. Individuals with spatial aphantasia may not necessarily have a deficit in object imagery. We discuss future research directions examining how spatial aphantasia may manifest behaviorally and neurologically, and how object and spatial aphantasia may be related

Lake Sevan Shoreline Change Assessment Using Multi-Temporal Landsat Images

Shoreline changes are important indicators of natural and manmade impacts on inland waters and particularly lakes. Man-induced changes in Lake Sevan water level during the 20th century affected not only the ecological status of the Sevan water but also near-shore areas. This article considers a long-term study of changes in Lake Sevan shoreline that occurred between 1973 and 2015. The Normalized Difference Water Index (NDWI) was applied to delineate the Sevan shoreline changes according to periods of lake water fluctuation from multi-temporal Landsat images and Historical changes in shorelines were analyzed with help of the Digital Shoreline Analysis System (DSAS) toolbox. Data obtained from the analysis have indicated that changes in the lake shoreline that occurred in different periods are similar to those in the lake water balance. Areas with the greatest shoreline changes have comparatively flat relief, so in the result of the lake water level raise vast forested areas were submerged. This study shows that application of multi-temporal spatial imagery and GIS methods can provide valuable information on time-and-space changes in the Sevan shoreline. Such information is important for monitoring Lake Sevan shoreline and nearshore changes.

Composite Image with a Geographic Information System Approach

Geographic information systems are used for spatial data processing. Data processing results from the system can be used as a tool to determine the condition of an area. By using several components one of which is coloring in geographical conditions in the region. The process of combining several colors to form a composite image that can show the clarity of the object. The results obtained from this study reveal the clarity of objects in the Banyumas Regency region by using levels in the band composition in spatial imagery

Selection of Informative Features to Highlight Areas of Waste Disposal by High Spatial Resolution Satellite Imagery

The paper deals with the problem of integrated interpretation of waste disposal facilities according to satellite imagery and ground truth monitoring, features of space images of landfills from various points of view: texture analysis, statistical properties, fractal analysis, color features, and the possibility of using machine learning methods. The main visual interpretive signs of landfills on optical and radar images of high spatial resolution are given. The fractal dimension of landfills was calculated for high resolution images using two models.

The contributions of spatial context and imagery to the recollection of single words.

A number of theories of hippocampal function have placed spatial context at the center of richly recollected memories, but the subjective and objective ways that spatial context underlies the recollection of single words has been largely overlooked and underexplained. In this study, we conducted three experiments to investigate the involvement of spatial context in the recollection of single words. In all three experiments, participants encoded single words with varying features such as location and color. The subjective experience of recollection was measured using remember/know judgments and participant self-report of the types of information they recollected about the words. Objectively, recollection was measured using source memory judgments for both spatial and non-spatial features associated with the words. Our results provide evidence that spatial context frequently accompanies the recollection of single, isolated words, reviving discussions on the role of the hippocampus in spatial and detailed recollection.

Textual Topographies: An Analysis of Spatial Imagery in Indian English Poetry

This research paper examines the role of spatial imagery in Indian English poetry, focusing on how poets from various regions of India use spatial metaphors to convey complex experiences of place, identity, and belonging. The study employs a qualitative, comparative analysis of selected poems from The Oxford Anthology of Modern Indian Poetry, edited by Vinay Dharwadker and A.K. Ramanujan. Through thematic analysis, the research identifies four primary themes—homeland, displacement, urbanization, and natural landscapes—and explores how these themes are uniquely expressed across different regional and cultural contexts. The findings reveal significant regional variations in the use of spatial imagery, with natural landscapes being more prominent in regions like Tamil Nadu, Kerala, and Northeast India, while urban metaphors are more prevalent in Maharashtra. The study also highlights the tension between tradition and modernity in Indian English poetry, showing how poets use spatial imagery to navigate and critique the impacts of modernization on their identities and communities. These findings contribute to a deeper understanding of the complexities of Indian English poetry and underscore the importance of considering regional and cultural diversity in literary analysis. The research has broader implications for postcolonial literary studies, offering new insights into how space and place shape the poetic imagination in India.

MULTI-SCALE AND SCALE DIMENSION PROPERTIES IN SPATIAL RASTER MODELLING – CONCEPT AND CURRENT IMPLEMENTATION

Abstract. Various users and applications required different abstraction details of spatial model either in vector or/and raster data types/models. Generating different model abstraction details (e.g. Level of Detail/LOD) produces various drawbacks especially for data model sharing among stakeholders or publics. Different abstraction detail or LOD means different details in geometry, semantic information, attributes as well as different accuracy provided within the vector model (e.g. a certain LOD). On the other hand, raster dataset with different resolutions on certain information or layer (e.g. elevation, land cover, spatial imagery, soil type, thematic raster map and others) could also be considered as multi-scale raster modelling which produces similar drawbacks with additional storage redundancy/consumption and updating works. There are some solutions for vector scale modelling such as CityGML (3D) and multi-scale or vario-scale (2D) modelling induce good solutions for vector; however, there are no solution for raster data type (or model) yet. Thus, a concept description in categorizing and defining multi-scale for multi-resolution raster dataset should be introduced. This paper basically highlights the similarity of spatial 2D vector and raster type GIS dataset, some introduction and properties of raster dataset which able to be defined it as the same level of vector LoD in scale modelling. This paper basically tries to kick off a new multi-scale domain in supporting spatial raster dataset (new idea), which will be then be extend/expand by related researchers near the future. Discussion on successful implementation of vector multi-scale model will be in the paper as well as existing multi-scale approach in storing raster dataset as the main content of the paper. Some potential analysis on related multi-scale raster and validation are also discussed to give brief idea on what is spatial raster capable of; especially to those who are new/not yet engage with this multi-scale spatial raster dataset.

Spatial Expressions of Ascetic Tendencies in the Qurʾan

AbstractIn this article, I propose viewing the early Qurʾanic movement as an expression of strong ascetic tendencies. More specifically, I suggest seeing aspects of Qurʾanic rhetorics as offering a specifically spatial expression to broader ascetic tendencies that characterized late antiquity as a whole, and which may be labeled “axial,” insofar as they can be traced back to the middle of the first millennium B.C.E., i.e., to the period coined by Karl Jaspers and others as “the Axial Age.” In the Qurʾan, rhetoric about striving for religious perfection takes on a spatial and horizontal expression, since the soteriological aspirations are formulated, to a certain extent, as a spatial ambition of “going out in the way of God.” As I suggest here, spatial imagery constitutes a prevalent theme throughout the Qurʾan and, based on an analysis of a number of examples, I argue that this spatial rhetoric indicates ascetic tendencies within the early Qurʾanic movement. The Qurʾan’s articulation of the tension between the believers and the surrounding world, including the tension between the muhājirūn (emigrants) and “those who stay behind,” is a prevalent theme throughout the text. This suggests that ascetic piety should be enacted spatially. In the last section of the article, I discuss the apparent change in semantics of two Qurʾanic terms that suggests the later Islamic exegetical tradition appears to favor an interpretation of these terms that allows for a more settled mentality and “stationary piety.”

TERRAIN MORPHOLOGY AS FACTOR OF LOCAL TEMPERATURES DISTRIBUTION IN UKRAINIAN CARPATHIANS

Ukrainian Carpathians like other mountainous areas are susceptible to the modern climate changes manifested mainly in accelerated warming. There are several main mechanisms of the orographic effects on temperature distribution, operating on different scales. The well-known and universally observed cooling with elevation is still often used as an over-simplified model of terrain relation to temperature regime, yet there are other significant orographic effects connected with the position of terrain features respective to incoming solar radiation angle and the large-scale movements of air masses. In Ukrainian Carpathians these effects are manifested mainly in more mild and less continental conditions on south-western macroslope that is also warmer due to larger solar radiation intake. Another important factor of temperature distribution is the formation of near-ground temperature inversions that often emerge in narrow valleys and inter-mountain troughs. The mapping of land surface temperatures can be effectively achieved by applying spatial imagery with thermal infrared bands; in our case, Landsat 8 images allowed to obtain detailed maps of temperature distribution in Bystrytsia river basin for three different seasons of the year. An analysis has been performed of relationships between temperatures and a set of morphometric parameters, by means of raster package and appropriate functions of R software environment. Rather strong negative relationship between elevation and temperature has been revealed in all the three cases. Direct relationship between slope and temperature was not confirmed, while potential incoming solar radiation values for the date and time of the imagery appeared to have a moderate effect on temperature, its effect varying from season to season. Key words: mountain climate; Carpathians; terrain morphology; temperature gradient; spatial analysis.

Deep Learning Algorithms Applied to the Classification of Video Meteor Detections

Abstract The application of a class of advanced machine learning techniques, namely deep learning, has been applied to automating the confirmation/classification of potential meteor tracks in video imagery. Deep learning is shown to perform remarkably well, even surpassing human performance, and will likely supplant the need for human visual inspection and review of collected meteor imagery. When applied to time series measurements of meteor track centroid positions and integrated intensities obtained from each video frame, a recurrent neural network (RNN) has achieved 98.1 per cent recall, which is defined as the number of true meteors properly classified as meteors. The RNN allowed only 2.1 per cent leakage, defined herein as the number of false positives that were incorrectly identified as meteors. The desire is to maximize recall to avoid missed orbit estimations, while also minimizing false alarms leaking through to the next processing stage of multi-site trajectory and orbit estimation. When two-dimensional spatial imagery is available or the temporal image sequence can be reconstructed, these results climb to 99.94 per cent recall and only 0.4 per cent leakage when employing a convolutional neural network (CNN). This has been further generalized from a baseline of interleaved analog video to modern progressive scan digital imagery with equivalent results. The trained CNN, nicknamed MeteorNet, will be used for post-detection automated screening of potential meteor tracks and explored in the future as a potential upstream meteor detector.

Hard work with soft spaces (and vice versa): problematizing the transforming planning spaces

ABSTRACTThis article studies spaces and spatial imageries in planning from two viewpoints. First, it discusses how contemporary planning paradigms contribute to a process that can be labelled the ‘softening of hard spaces’. This means that typically old, well-established (planning) spaces with relatively hard administrative borders become redefined and treated in planning practice as soft entities with fuzzier or more porous borders. Second, it discusses how new soft spaces – such as gateways, new cross-border supranational spaces and ad hoc regional spaces – tend to simultaneously harden through intensifying institutional practices and discourses, as well as because of the need to define what is included and excluded in such new spatial structures/networks. These two processes, the softening of hard spaces and the hardening of soft spaces, are then scrutinized in tandem, and a conceptualization of intermediary hybrid planning spaces is proposed. This conceptual opening, labelled ‘penumbral’ space/border, is then examined. The explanatory value of these arguments is demonstrated by comparing the transformation of Northern Ostrobothnia, an old, well-established region in Finland, and the mobilization of Bothnian Arc, a new soft space stretching across the Swedish-Finnish border.

Forest inventory sensivity to UAS-based image processing algorithms

Frequent and accurate estimation of forest structure parameters, such as number of trees per hectare or total height, are mandatory for sustainable forest management. Unmanned aircraft system (UAS) equipped with inexpensive sensors can be used to monitor and measure forest structure. The detailed information provided by the UAS allows tree level forest inventory. However, tree identification depends on a variety of parameters defining the image processing and tree segmentation algorithms. The objective of our study was to identify parameter combinations that accurately delineated trees and their heights. We evaluated the impact of different tree segmentation and point cloud generation algorithms on forest inventory from imagery collected with a UAS over a mature Douglas-fir plantation forest. We processed the images with two commonly used commercial software packages, Agisoft PhotoScan and Pix4Dmapper, both implementing image processing algorithms called Structure from Motion. For each software we generated photogrammetric point clouds by varying the parameters defining the implementation. We segmented individual trees and heights using three tree algorithms: Variable Window Filter, Graph-Theoretical, and Watershed Segmentation. We assessed the impact of image processing algorithms on forest inventory by comparing the estimated trees with trees manually identified from the point clouds. We found that the type of tree segmentation and image processing algorithms have a significant effect in accurately identifying trees. For tree height estimation, we found strong evidence that image processing algorithms had significant effects, whereas tree segmentation algorithms did not significantly affect tree height estimation.These findings may be of interest to others that are using high-resolution spatial imagery to estimate forest inventory parameters.