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Search Results: 1 - 10 of 11747 matches for " Object-Spatial Imagery Style "
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Object-Spatial Imagery Types of Japanese College Students  [PDF]
Masahiro Kawahara, Kazuo Matsuoka
Psychology (PSYCH) , 2013, DOI: 10.4236/psych.2013.43024

This study investigated the object-spatial imagery types found among Japanese college students. First, we examined the descriptive statistics of the Japanese version of the Object-Spatial Imagery Questionnaire object-spatial imagery scales, which measure respondents’ tendencies with respect to object-spatial imagery types. Although the means of these subscales were lower than those of the original versions, the raw score distributions and gender differences were similar to those obtained using the original version. Additionally, we compared imagery types among students in seven different academic departments. Specifically, the results showed specific patterns of imagery type among students in each department, indicating that the object-spatial imagery type model is applicable to Japanese college students and that individual imagery type data would be helpful for career guidance.

Hemispheric Differences within the Fronto-Parietal Network Dynamics Underlying Spatial Imagery
Alexander T. Sack,Teresa Schuhmann
Frontiers in Psychology , 2012, DOI: 10.3389/fpsyg.2012.00214
Abstract: Spatial imagery refers to the inspection and evaluation of spatial features (e.g., distance, relative position, configuration) and/or the spatial manipulation (e.g., rotation, shifting, reorienting) of mentally generated visual images. In the past few decades, psychophysical as well as functional brain imaging studies have indicated that any such processing of spatially coded information and/or manipulation based on mental images (i) is subject to similar behavioral demands and limitations as in the case of spatial processing based on real visual images, and (ii) consistently activates several nodes of widely distributed cortical networks in the brain. These nodes include areas within both, the dorsal fronto-parietal as well as ventral occipito-temporal visual processing pathway, representing the “what” versus “where” aspects of spatial imagery. We here describe evidence from functional brain imaging and brain interference studies indicating systematic hemispheric differences within the dorsal fronto-parietal networks during the execution of spatial imagery. Importantly, such hemispheric differences and functional lateralization principles are also found in the effective brain network connectivity within and across these networks, with a direction of information flow from anterior frontal/premotor regions to posterior parietal cortices. In an attempt to integrate these findings of hemispheric lateralization and fronto-to-parietal interactions, we argue that spatial imagery constitutes a multifaceted cognitive construct that can be segregated in several distinct mental sub processes, each associated with activity within specific lateralized fronto-parietal (sub) networks, forming the basis of the here proposed dynamic network model of spatial imagery.
Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data
Txomin Hermosilla,Luis A. Ruiz,Jorge A. Recio,Javier Estornell
Remote Sensing , 2011, DOI: 10.3390/rs3061188
Abstract: In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.
Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis
Maggi Kelly,Samuel D. Blanchard,Ellen Kersten,Kevin Koy
Remote Sensing , 2011, DOI: 10.3390/rs3112321
Abstract: The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or “OBIA”) can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.
Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis  [PDF]
Bangyan Zhou, Xiaopei Wu, Lei Zhang, Zhao Lv, Xiaojing Guo
Journal of Biosciences and Medicines (JBM) , 2014, DOI: 10.4236/jbm.2014.22007

Independent Component Analysis (ICA) was often used to separate movement related independent components (MRICs) from Electroencephalogram (EEG) data. However, to obtain robust spatial filters, complex characteristic features, which were manually selected in most cases, have been commonly used. This study proposed a new simple algorithm to extract MRICs automatically, which just utilized the spatial distribution pattern of ICs. The main goal of this study was to show the relationship between spatial filters performance and designing samples. The EEG data which contain mixed brain states (preparing, motor imagery and rest) were used to design spatial filters. Meanwhile, the single class data was also used to calculate spatial filters to assess whether the MRICs extracted on different class motor imagery spatial filters are similar. Furthermore, the spatial filters constructed on one subject’s EEG data were applied to extract the others’ MRICs. Finally, the different spatial filters were then applied to single-trial EEG to extract MRICs, and Support Vector Machine (SVM) classifiers were used to discriminate left handright-hand and foot imagery movements of BCI Competition IV Dataset 2a, which recorded four motor imagery data of nine subjects. The results suggested that any segment of finite motor imagery EEG samples could be used to design ICA spatial filters, and the extracted MRICs are consistent if the position of electrodes are the same, which confirmed the robustness and practicality of ICA used in the motor imagery Brain Computer Interfaces (MI-BCI) systems.

Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK)
Daniel Jones,Stephen Pike,Malcolm Thomas,Denis Murphy
Remote Sensing , 2011, DOI: 10.3390/rs3020319
Abstract: Japanese Knotweed s.l. taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS) in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs) of the world. The current paper utilises the Object-Based Image Analysis (OBIA) approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible?band (RGB) aerial photography in order to detect Japanese Knotweed s.l. taxa in Wales (UK). An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk?based studies. Further, these results may be incorporated into Geographical Information Systems (GIS) research as they are readily transferable as vector polygons (shapefiles). The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively) limited technical requirements necessary to conduct further analyses.
Development of a Generic Model for the Detection of Roof Materials Based on an Object-Based Approach Using WorldView-2 Satellite Imagery  [PDF]
Ebrahim Taherzadeh, Helmi Z. M. Shafri
Advances in Remote Sensing (ARS) , 2013, DOI: 10.4236/ars.2013.24034

The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.

Scale-based spatial data model for GIS
重庆邮电大学学报(自然科学版) , 2004,
Scale-based spatial data model for GIS
重庆邮电大学学报(自然科学版) , 2004,
Spatio-Temporal Analysis of Landuse/Landcover Change of District Pishin Using Satellite Imagery and GIS  [PDF]
Sanaullah Khan, Said Qasim, Romana Ambreen, Zia-Ul-Haq Syed
Journal of Geographic Information System (JGIS) , 2016, DOI: 10.4236/jgis.2016.83031
Abstract: Detecting change on the face of the globe using GIS (Geographic Information System) aided by remotely sensed imagery is now becoming an indispensable tool in managing the resources of our planet. The present study with the help of GIS and remote sensing (RS) is also a similar attempt in recording and quantifying change in land use and land cover in district Pishin both in spatial and temporal extents. Satellite imagery was acquired from the USGS official website from three LANDSAT satellites. Theses satellites are LANDSAT 5, LANDSAT7 and LANDSAT 8. The data were acquired for the years 1992, 2003 and 2013. Satellite imagery was processed in ArcMap 10.1 and maximum likelihood supervised image classification was applied in reaching the goal of detecting change. The result of the analysis revealed that built-up area was increased by 5.84%; vegetation was increased by 3.89%; water bodies were increased by 0.05% and bare surfaces were decreased by 9.78%. The decrease in the barren surfaces was attributed to the increase in vegetation and built-up area which replaced the barren land in the study area. This paper also shows the significance and potential of digital change detection methods in managing the resources of our environment and keeping an eye on the land use and land cover of our Earth.
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