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Journal articles on the topic 'Imagerie Subsalt'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Wang, Lin, Hisao-Chi Li, Bai Xue, and Chein-I. Chang. "Constrained Band Subset Selection for Hyperspectral Imagery." IEEE Geoscience and Remote Sensing Letters 14, no. 11 (2017): 2032–36. http://dx.doi.org/10.1109/lgrs.2017.2749209.

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Wang, Lin, Chein-I. Chang, Li-Chien Lee, et al. "Band Subset Selection for Anomaly Detection in Hyperspectral Imagery." IEEE Transactions on Geoscience and Remote Sensing 55, no. 9 (2017): 4887–98. http://dx.doi.org/10.1109/tgrs.2017.2681278.

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Zhao, Yong-Qiang, Lei Zhang, and Seong G. Kong. "Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification." IEEE Transactions on Geoscience and Remote Sensing 49, no. 2 (2011): 747–56. http://dx.doi.org/10.1109/tgrs.2010.2059707.

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Morenikeji, G. B., O. O. Idowu, B. M. Adeleye, O. R. Bankole, and T. W. Anjide. "Effects of Population Increase on Peri-Urban Land Growth in Asa Local Government Area, Kwara State." Environmental Technology and Science Journal 14, no. 1 (2023): 180–88. http://dx.doi.org/10.4314/etsj.v14i1.19.

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The rapid growth of world population and its agglomeration in cities and towns around the world is affecting the longterm outlook for humanity, in such that the process of urban growth and the effect at the peri-urban areas are universal, occurring all over the world. This study aimed at assessing the effects of population increase on the growth of periurban land in Asa local government area, Kwara State. Secondary data, via satellite imageries covering 2000, 2010 and 2021 were mainly used in analyzing the changes that occurred within twenty years. Remote sensing and GIS approaches to satellit

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Williams, Sarah E., and Jennifer Cumming. "Measuring Athlete Imagery Ability: The Sport Imagery Ability Questionnaire." Journal of Sport and Exercise Psychology 33, no. 3 (2011): 416–40. http://dx.doi.org/10.1123/jsep.33.3.416.

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This research aimed to develop and provide initial validation of the Sport Imagery Ability Questionnaire (SIAQ). The SIAQ assesses athletes’ ease of imaging different types of imagery content. Following an extensive pilot study, 375 athletes completed a 20-item SIAQ in Study 1. Exploratory factor analysis revealed a 4-factor model assessing skill, strategy, goal, and affect imagery ability. Confirmatory factor analysis (CFA) established this 4-factor structure in Study 2 (N = 363 athletes). In Study 3 (N = 438 athletes), additional items were added to create a fifth mastery imagery subscale th

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Liu, Yufei, Xiaorun Li, Ziqiang Hua, and Liaoying Zhao. "EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection." Remote Sensing 13, no. 18 (2021): 3602. http://dx.doi.org/10.3390/rs13183602.

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Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing. However, most of the existing BS methods fail to fully consider the interaction between spectral bands and cannot comprehensively consider the representativeness and redundancy of the selected band subset. To solve these problems, we propose an unsupervised effective band attention reconstruction framework for band selection (EBARec-BS) in this article. The framework utilizes the EBARec network to learn the representativeness of each band to t

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Di, Wei, Quan Pan, Yong-qiang Zhao, and Lin He. "Anomaly Target Detection in Hyperspectral Imagery Based on Band Subset Fusion by Fuzzy Integral." Journal of Electronics & Information Technology 30, no. 2 (2011): 267–71. http://dx.doi.org/10.3724/sp.j.1146.2006.01140.

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Corlett, John T., John Anton, Steve Kozub, and Michel Tardif. "Is Locomotor Distance Estimation Guided by Visual Imagery?" Perceptual and Motor Skills 69, no. 3_suppl (1989): 1267–72. http://dx.doi.org/10.2466/pms.1989.69.3f.1267.

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70 subjects were tested for their visual subscale scores on the Movement Imagery Questionnaire and also for their ability to walk, without vision, to a previously viewed target location 9 m away. Imagery ability was hypothesized to correlate with accuracy of “blind” target-directed walking which the literature suggests, without empirical support, is imagery-dependent. No support for this hypothesis was found. Low, medium, and high imagers showed no differences in ability to reproduce target distance accurately or consistently by walking the estimated distance without further visual updating. T

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Corlett, John T., John Anton, Steve Kozub, and Michel Tardif. "Is Locomotor Distance Estimation Guided by Visual Imagery?" Perceptual and Motor Skills 69, no. 3-2 (1989): 1267–72. http://dx.doi.org/10.1177/00315125890693-237.

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70 subjects were tested for their visual subscale scores on the Movement Imagery Questionnaire and also for their ability to walk, without vision, to a previously viewed target location 9 m away. Imagery ability was hypothesized to correlate with accuracy of “blind” target-directed walking which the literature suggests, without empirical support, is imagery-dependent. No support for this hypothesis was found. Low, medium, and high imagers showed no differences in ability to reproduce target distance accurately or consistently by walking the estimated distance without further visual updating. T

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Adam, Fathalrahman, Thomas Esch, and Mihai Datcu. "Feature Investigation for Large Scale Urban Detection Using Landsat Imagery." Proceedings 2, no. 7 (2018): 349. http://dx.doi.org/10.3390/ecrs-2-05162.

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Many works dealing with the problem of urban detection at large scale have been published, but very little attention has been paid to the investigation of the features’ relative importance. Feature selection is known to be an NP-hard problem, which means it can not be solved in polynomial time, but there are many heuristics suggested to approximate the solution. In this paper, a survey of the features used for large scale urban detection is presented, then the question of finding the best subset of features is investigated. Using Landsat scenes of five urban areas, most common features were ex

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Baig, Muhammad Zeeshan, Nauman Aslam, Hubert P. H. Shum, and Li Zhang. "Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG." Expert Systems with Applications 90 (December 2017): 184–95. http://dx.doi.org/10.1016/j.eswa.2017.07.033.

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Wang, Jie, Zuren Feng, Xiaodong Ren, Na Lu, Jing Luo, and Lei Sun. "Feature subset and time segment selection for the classification of EEG data based motor imagery." Biomedical Signal Processing and Control 61 (August 2020): 102026. http://dx.doi.org/10.1016/j.bspc.2020.102026.

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Badau, Adela. "Mental Imagery and Attentional Style in Senior Mini-Football Players." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 2 (2024): 46–66. http://dx.doi.org/10.18662/brain/15.2/563.

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The purpose of this study was to determine the level of mental imagery ability and attentional style in senior mini-football players and to identify mental behavioral patterns. The study included a number of 38 senior mini-football players, with an average age of 27.36 years, with a playing experience in the super league of 9.38 years. In the study, two standardized questionnaires were applied, one aimed at identifying the level of mental imagery called the Questionnaire for the Assessment of Mental Imaginative Abilities in Athletes (QMIA) with 16 items and one for identifying the attentional

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Adela, Badau. "Mental Imagery and Attentional Style in Senior Mini-Football Players." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 2 (2024): 46–66. https://doi.org/10.18662/brain/15.2/563.

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The purpose of this study was to determine the level of mental imagery ability and attentional style in senior mini-football players and to identify mental behavioral patterns. The study included a number of 38 senior mini-football players, with an average age of 27.36 years, with a playing experience in the super league of 9.38 years. In the study, two standardized questionnaires were applied, one aimed at identifying the level of mental imagery called the Questionnaire for the Assessment of Mental Imaginative Abilities in Athletes (QMIA) with 16 items and one for identifying the attentional

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Amorim, André, Bruno Travassos, and Pedro Mendes. "Imagery ability in Boccia: Comparison among federate athletes from different medical sport groups." Motricidade 13, no. 4 (2018): 46. http://dx.doi.org/10.6063/motricidade.11780.

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The aim of this study was to analyse and compare movement visualization ability in federate and non-federate Boccia athletes, and among federate Boccia medical sport groups. Forty-two Boccia athletes (Federate N = 24; Non-federate N = 18) at an average age of 35.8 (SD = 11.19) participated in this study. The Portuguese version of Movement Imagery Questionnaire - 3 (MIQ-3), was used for this study. The participants were evaluated on the internal and external visual imagery. Statistics was carried out following the method of interference based on the magnitude of the effects. Results showed a gr

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Budnik-Przybylska, Dagmara, Natalia Rogowska, and Aleksandra Kantypowicz. "Imagery Ability and Body Image as Psychological Factors Connected to Predisposition to Orthorexia Among Female Team Sport Athletes." Studies in Sport Humanities 32 (November 27, 2024): 13–19. http://dx.doi.org/10.5604/01.3001.0054.8582.

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Purpose: The aim of this study was to examine the associations between imagery ability, body image, and orthorexia. Methods: One hundred female players of team sports such as basketball, volleyball and handball with experience of over 11 years (M=11.48, SD=2.85) took part in the study. The participants filled in an information form, including their age and sports training experience, as well as a set of questionnaires concerning orthorexia, imagery ability, and body image. The Sport Imagery Ability Questionnaire (SIAQ), Vividness Movement Imagery Questionnaire-2 (VMIQ-2), The Body Esteem Scale

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Hewahi, Nabil M., and Eyad A. Alashqar. "Wrapper Feature Selection based on Genetic Algorithm for Recognizing Objects from Satellite Imagery." Journal of Information Technology Research 8, no. 3 (2015): 1–20. http://dx.doi.org/10.4018/jitr.2015070101.

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Object recognition is a research area that aims to associate objects to categories or classes. The recognition of object specific geospatial features, such as roads, buildings and rivers, from high-resolution satellite imagery is a time consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Feature selection is the task of selecting a small subset from original features that can achieve maximum classification accuracy and reduce data dimensionality. This subset of features has some very important benefits like, it reduces computational complexity of

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Saner, Asli, and Min Chen. "Music Similarity Detection Through Comparative Imagery Data." Applied Sciences 15, no. 14 (2025): 7706. https://doi.org/10.3390/app15147706.

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In music, plagiarism has been an important but troubled issue, which becomes ever more critical with the widespread usage of generative AI tools. Meanwhile, the development of techniques for music similarity detection has been hampered by the scarcity of legally verified data on plagiarism. In this paper, we present a technical solution for training music similarity detection models through the use of comparative imagery data. With the aid of feature-based analysis and data visualization, we conducted experiments to analyze how different music features may contribute to the judgment of plagiar

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Anand, Raju, Sathishkumar Samiaappan, Shanmugham Veni, Ethan Worch, and Meilun Zhou. "Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization." Journal of Imaging 8, no. 5 (2022): 126. http://dx.doi.org/10.3390/jimaging8050126.

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In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classi

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Samadzadegan, Farhad, and Hadiseh Hasani. "Determination Optimum SVMs Classifiers for Hyperspectral Imagery Based on Ant Colony Optimization." Key Engineering Materials 500 (January 2012): 792–98. http://dx.doi.org/10.4028/www.scientific.net/kem.500.792.

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Referring to robustness of SVMs in high dimensional space, they are reliable tools for classification of hyperspectral imagery. However their performance is directly affected by two aspects: parameter determination and optimum feature subset selection. According to capacity of population based meta-heuristic optimization algorithm such as Ant Colony Optimization (ACO), they can find optimum or near optimum solution in complex optimization problems. This paper evaluates the potential of Binary ACO (BACO) in parameter determination, feature selection and both of them simultaneously in SVMs based

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Suárez Rozo, Manuel Enrique, Sara Trapero-Asenjo, Daniel Pecos-Martín, et al. "Reliability of the Spanish Version of the Movement Imagery Questionnaire-3 (MIQ-3) and Characteristics of Motor Imagery in Institutionalized Elderly People." Journal of Clinical Medicine 11, no. 20 (2022): 6076. http://dx.doi.org/10.3390/jcm11206076.

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Motor imagery (MI) training is increasingly used to improve the performance of specific motor skills. The Movement Imagery Questionnaire-3 (MIQ-3) is an instrument for assessing MI ability validated in Spanish although its reliability has not yet been studied in the elderly population. The main objective of this study was to test its reliability in institutionalized elderly people. Secondarily, we studied whether there are differences according to gender and age in MI ability (measured by the MIQ-3) and in temporal congruency (measured by mental chronometry of elbow and knee flexion-extension

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Razvaliaeva, A. Y. "Validating the Plymouth Sensory Imagery Questionnaire in the Russian Sample." Experimental Psychology (Russia) 17, no. 3 (2024): 144–57. http://dx.doi.org/10.17759/exppsy.2024170310.

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<p>Mental imagery (secondary images, mental representations) is defined as the contents of consciousness that are phenomenologically close to sensory images but arise without external stimulation of the senses. The paper presents the results of validating the Russian version of the Plymouth Sensory Imagery Questionnaire that measures the self-reported vividness of mental images pertaining to different sensory modalities. The sample consisted of 253 participants 17&ndash;50 years old. The original 7 scales (vision, sound, smell, taste, touch, bodily sensation, and emotions) and the to

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Samadzadega, F., and H. Hasani. "DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 651–56. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-651-2015.

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Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performanc

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Hatakeyama, Takao. "Associations between Autistic-like Traits and Imagery Ability." Vision 8, no. 1 (2024): 13. http://dx.doi.org/10.3390/vision8010013.

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This article examines empirical associations between qualities of the imagination, mental imagery, ands cognitive abilities with special reference to autism. This study is the first to explore the empirical relationships between autistic-like traits and tests of imagery differences. Imaginative impairments and distinctive sensory characteristics in individuals with autism spectrum disorder (ASD) should be reflected in their interactions with mental imagery. However, the relationship between ASD and imaging traits remains unclear. Based on the hypothesis that the degree of autistic-like traits

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Yang, Lingbo, Lamin Mansaray, Jingfeng Huang, and Limin Wang. "Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery." Remote Sensing 11, no. 5 (2019): 514. http://dx.doi.org/10.3390/rs11050514.

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Geographic object-based image analysis (GEOBIA) has been widely used in the remote sensing of agricultural crops. However, issues related to image segmentation, data redundancy and performance of different classification algorithms with GEOBIA have not been properly addressed in previous studies, thereby compromising the accuracy of subsequent thematic products. It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource satellite imagery. For this

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Chang, Hongli, and Jimin Yang. "Automated Selection of a Channel Subset Based on the Genetic Algorithm in a Motor Imagery Brain-Computer Interface System." IEEE Access 7 (2019): 154180–91. http://dx.doi.org/10.1109/access.2019.2944938.

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Probeck, Markus, Ralf Ludwig, and Wolfram Mauser. "Fusion of NOAA-AVHRR imagery and geographical information system techniques to derive subscale land cover information for the upper Danube watershed." Hydrological Processes 19, no. 12 (2005): 2407–18. http://dx.doi.org/10.1002/hyp.5892.

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Hoffman, Jay, Steven Ackerman, Yinghui Liu, and Jeffrey Key. "The Detection and Characterization of Arctic Sea Ice Leads with Satellite Imagers." Remote Sensing 11, no. 5 (2019): 521. http://dx.doi.org/10.3390/rs11050521.

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Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subse

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Wang, Yulei, Lin Wang, Hongye Xie, and Chein-I. Chang. "Fusion of Various Band Selection Methods for Hyperspectral Imagery." Remote Sensing 11, no. 18 (2019): 2125. http://dx.doi.org/10.3390/rs11182125.

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This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, nBS. Since each BS method has its own merit in finding the desired bands, various BS methods produce different band subsets with the same nBS. In order to take advantage of these different band subsets, the proposed BSF is performed by first finding the union of all band subsets produced by a set of BS methods as a joint band subset (JBS). Due to the fact that a band selected by one BS method in JBS may be also s

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Kiala, Zolo, Onisimo Mutanga, John Odindi, and Kabir Peerbhay. "Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed." Remote Sensing 11, no. 16 (2019): 1892. http://dx.doi.org/10.3390/rs11161892.

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In the recent past, the volume of spatial datasets has significantly increased. This is attributed to, among other factors, higher sensor temporal resolutions of the recently launched satellites. The increased data, combined with the computation and possible derivation of a large number of indices, may lead to high multi-collinearity and redundant features that compromise the performance of classifiers. Using dimension reduction algorithms, a subset of these features can be selected, hence increasing their predictive potential. In this regard, an investigation into the application of feature s

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Safarov, Furkat, Kuchkorov Temurbek, Djumanov Jamoljon, et al. "Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture." Sensors 22, no. 24 (2022): 9784. http://dx.doi.org/10.3390/s22249784.

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Currently, there is a growing population around the world, and this is particularly true in developing countries, where food security is becoming a major problem. Therefore, agricultural land monitoring, land use classification and analysis, and achieving high yields through efficient land use are important research topics in precision agriculture. Deep learning-based algorithms for the classification of satellite images provide more reliable and accurate results than traditional classification algorithms. In this study, we propose a transfer learning based residual UNet architecture (TL-ResUN

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Kheira, Djelloul, and M. Beladgham. "Performance of channel selection used for Multi-class EEG signal classification of motor imagery." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1305. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1305-1312.

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<p>In this paper, a study of a non-invasive brain-machine interfaces for the classification of 4 imaginary are presented. Performance comparisons using time-frequency analysis between the Linear Discriminant Analysis motor activities (left hand, right hand, foot, tongue) with the BCI competition III dataset IIIa is (LDA), the Support Vector Machine (SVM) and the K-Nearest Neighbors (KNN) algorithms have been carried. The number and position of electrodes for each subject were investigated to provide an improvement for the classification accuracy of the algorithm. Results show that the el

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Monsma, Eva V., and Lynnette Y. Overby. "The Relationship between Imagery and Competitive Anxiety in Ballet Auditions." Journal of Dance Medicine & Science 8, no. 1 (2004): 11–18. http://dx.doi.org/10.1177/1089313x0400800102.

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Aligned with competitive anxiety research in athletics, this study explored audition anxiety and the role of imagery in the anxiety-performance relationship among 131 female auditioning ballet dancers. A better understanding of how auditioning dancers experience anxiety and associated image content can help train dancers preparing for anxiety-provoking, high-stakes performances. The CSAI-2 assessing competitive state anxiety and confidence and the SIQ assessing the cognitive and motivational functions of imagery were slightly modified for administration in the audition context. The MIQ-R was u

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Yassine, H., K. Tout, and M. Jaber. "IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 369–76. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-369-2021.

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Abstract. Machine learning (ML) has proven useful for a very large number of applications in several domains. It has realized a remarkable growth in remote-sensing image analysis over the past few years. Deep Learning (DL) a subset of machine learning were applied in this work to achieve a better classification of Land Use Land Cover (LULC) in satellite imagery using Convolutional Neural Networks (CNNs). EuroSAT benchmarking data set is used as training data set which uses Sentinel-2 satellite images. Sentinel-2 provides images with 13 spectral feature bands, but surprisingly little attention

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Akay, A. E., B. Gencal, and İ. Taş. "SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 31–35. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-31-2017.

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This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant l

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Abadal, Saüc, Luis Salgueiro, Javier Marcello, and Verónica Vilaplana. "A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery." Remote Sensing 13, no. 22 (2021): 4547. http://dx.doi.org/10.3390/rs13224547.

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There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input imag

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Hari Krishna, D., I. A.Pasha, and T. Satya Savithri. "Multiclass classification of motor imagery EEG signals using ensemble classifiers & cross-correlation." International Journal of Engineering & Technology 7, no. 2.6 (2018): 163. http://dx.doi.org/10.14419/ijet.v7i2.6.10144.

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To communicate without any muscle movement and purely based on brain signal has been the goal of Brain computer interfacing (BCI). Recent BCI based studies reported more and more accurate detection of brain states. This paper proposes a study that detects EEG signal belonging todifferent imaginary motor activities (Right leg, right hand, left leg and left hand). The Electroencephalogram (EEG) signal has been conditioned by band pass filter (BPF) to improve signal to noise ratio (SNR). The proposed method is based on similarity between signals to extract features. For measuring the similarity b

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Meyer, J., D. Rettenmund, and S. Nebiker. "LONG-TERM VISUAL LOCALIZATION IN LARGE SCALE URBAN ENVIRONMENTS EXPLOITING STREET LEVEL IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 57–63. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-57-2020.

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Abstract. In this paper, we present our approach for robust long-term visual localization in large scale urban environments exploiting street level imagery. Our approach consists of a 2D-image based localization using image retrieval (NetVLAD) to select reference images. This is followed by a 3D-structure based localization with a robust image matcher (DenseSfM) for accurate pose estimation. This visual localization approach is evaluated by means of the ‘Sun’ subset of the RobotCar seasons dataset, which is part of the Visual Localization benchmark. As the results on the RobotCar benchmark dat

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Chen, Buo-Fu, Boyo Chen, Hsuan-Tien Lin, and Russell L. Elsberry. "Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks." Weather and Forecasting 34, no. 2 (2019): 447–65. http://dx.doi.org/10.1175/waf-d-18-0136.1.

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Abstract Accurately estimating tropical cyclone (TC) intensity is one of the most critical steps in TC forecasting and disaster warning/management. For over 40 years, the Dvorak technique (and several improved versions) has been applied for estimating TC intensity by forecasters worldwide. However, the operational Dvorak techniques primarily used in various agencies have several deficiencies, such as inherent subjectivity leading to inconsistent intensity estimates within various basins. This collaborative study between meteorologists and data scientists has developed a deep-learning model usi

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Athanasiou, Alkinoos, Nikos Terzopoulos, Niki Pandria, et al. "Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury." Neural Plasticity 2018 (2018): 1–20. http://dx.doi.org/10.1155/2018/9354207.

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Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple v

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Duarte-Carvajalino, Julio Martin, Elías Alexander Silva-Arero, Gerardo Antonio Góez-Vinasco, Laura Marcela Torres-Delgado, Oscar Dubán Ocampo-Paez, and Angela María Castaño-Marín. "Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms." Horticulturae 7, no. 7 (2021): 176. http://dx.doi.org/10.3390/horticulturae7070176.

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This work presents quantitative detection of water stress and estimation of the water stress level: none, light, moderate, and severe on potato crops. We use hyperspectral imagery and state of the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional neural networks, support vector machines, extreme gradient boost, and AdaBoost. The detection and estimation of water stress in potato crops is carried out on two different phenological stages of the plants: tubers differentiation and maximum tuberization. The machine learning algorithms are trained with a s

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Astiti, Sagung Putri Chandra, Takahiro Osawa, and I. Wayan Nuarsa. "IDENTIFICATION OF SHORELINE CHANGES USING SENTINEL 2 IMAGERY DATA IN CANGGU COASTAL AREA." ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) 13, no. 2 (2019): 191. http://dx.doi.org/10.24843/ejes.2019.v13.i02.p07.

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Coastal areas in the Canggu and Seminyak areas located in Badung Regency, Bali Province are very attractive tourism. The development of tourism has an impact on coastal conditions. The coastal conditions analyzed are changes in coastline that occurred during 2015-2019 using remote sensing. The satellite image data used in the analysis is Sentinel 2A image data that can be accessed for free with a spatial resolution of 10 meters. Image data processing is divided into three stages, namely preprocessing, processing, and post processing using Sentinel Application Platform (SNAP) software. The prep

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Angel, Yoseline, Rasmus Houborg, and Matthew F. McCabe. "Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery." Remote Sensing 11, no. 10 (2019): 1145. http://dx.doi.org/10.3390/rs11101145.

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One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral

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Lewandowski, Wendy, Ann Jacobson, Patrick A. Palmieri, Thomas Alexander, and Richard Zeller. "Biological Mechanisms Related to the Effectiveness of Guided Imagery for Chronic Pain." Biological Research For Nursing 13, no. 4 (2010): 364–75. http://dx.doi.org/10.1177/1099800410386475.

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Specific aims of this pilot study were to (a) determine the effect of a guided imagery (GI) intervention over an 8-week period on pain and pain disability in a sample of persons with chronic noncancer pain (CNCP) and (b) analyze the mediating effects of neuroendocrine and neuroimmune functioning on the effectiveness of GI on outcome variables. A simple interrupted time-series design (12-week period) was used. GI was introduced at Week 4 and used daily by 25 participants for the remaining 8 weeks. Measures of pain and pain disability were obtained at the beginning of the study period and at six

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Moradi, F., A. Zarei, S. Ranjbar, and S. Homayouni. "WHEAT BIOMASS ESTIMATION FROM UAV IMAGERY USING AN ENSEMBLE LEARNING APPROACH WITH BAYESIAN OPTIMIZATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 14, 2023): 515–22. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-515-2023.

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Abstract. Wheat is one of the most important food supply and food security globally, especially in developing countries. Therefore, predicting the performance and determining the factors that affect the production of this product is very important. Biomass is one of the crop’s most important biophysical parameters, and its correct estimation can help improve accurate monitoring of growth and crop performance forecasting. With the recent advances in remote sensing, access to aerial images taken by unmanned aerial vehicles (UAV) for monitoring crops has been provided. This study investigates the

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Dobrinić, D., M. Gašparović, and D. Medak. "EVALUATION OF FEATURE SELECTION METHODS FOR VEGETATION MAPPING USING MULTITEMPORAL SENTINEL IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 485–91. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-485-2022.

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Abstract. With the recent advances in remote sensing technologies for Earth observation (EO), many different remote sensors (e.g., optical, radar) collect data with distinctive properties. EO data have been employed to monitor croplands and forested areas, oceans and seas, urban settlements, and natural hazards. The spectral, spatial, and temporal resolutions of remote sensors have been continuously improving, making geospatial monitoring more accurate and comprehensive than ever before. To tackle this issue, various variable selection methods (e.g., filter, wrapper, and embedded methods) are

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Vilela, Emerson Ferreira, Cileimar Aparecida da Silva, Jéssica Mayara Coffler Botti, et al. "Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning." AgriEngineering 6, no. 3 (2024): 3174–86. http://dx.doi.org/10.3390/agriengineering6030181.

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The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indice

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Feng, Ruyi, Lizhe Wang, and Yanfei Zhong. "Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery." Remote Sensing 10, no. 10 (2018): 1546. http://dx.doi.org/10.3390/rs10101546.

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Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the optimal subset of signatures in this spectral library using the sparse regression technique, and has greatly improved the estimation of fractional abundances in ubiquitous mixed pixels. Since the potentially large standard spectral library can be given a priori, the most challenging task is to compute the regression coefficients, i.e., the fractio

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Winiwarter, Lukas, Nicholas C. Coops, Alex Bastyr, et al. "Extraction of Forest Road Information from CubeSat Imagery Using Convolutional Neural Networks." Remote Sensing 16, no. 6 (2024): 1083. http://dx.doi.org/10.3390/rs16061083.

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Forest roads provide access to remote wooded areas, serving as key transportation routes and contributing to human impact on the local environment. However, large animals, such as bears (Ursus sp.), moose (Alces alces), and caribou (Rangifer tarandus caribou), are affected by their presence. Many publicly available road layers are outdated or inaccurate, making the assessment of landscape objectives difficult. To address these gaps in road location data, we employ CubeSat Imagery from the Planet constellation to predict the occurrence of road probabilities using a SegNet Convolutional Neural N

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Lv, Chengzhe, Yuefeng Lu, Miao Lu, et al. "A Classification Feature Optimization Method for Remote Sensing Imagery Based on Fisher Score and mRMR." Applied Sciences 12, no. 17 (2022): 8845. http://dx.doi.org/10.3390/app12178845.

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In object-oriented remote sensing image classification experiments, the dimension of the feature space is often high, leading to the “dimension disaster”. If a reasonable feature selection method is adopted, the classification efficiency and accuracy of the classifier can be improved. In this study, we took GF-2 remote sensing imagery as the research object and proposed a feature dimension reduction algorithm combining the Fisher Score and the minimum redundancy maximum relevance (mRMR) feature selection method. First, the Fisher Score was used to construct a feature index importance ranking,

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