Auswahl der wissenschaftlichen Literatur zum Thema „Pixel-Object classification“
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Zeitschriftenartikel zum Thema "Pixel-Object classification"
Bernardini, A., E. Frontoni, E. S. Malinverni, A. Mancini, A. N. Tassetti und P. Zingaretti. „Pixel, object and hybrid classification comparisons“. Journal of Spatial Science 55, Nr. 1 (Juni 2010): 43–54. http://dx.doi.org/10.1080/14498596.2010.487641.
Der volle Inhalt der QuelleMakinde, Esther Oluwafunmilayo, Ayobami Taofeek Salami, James Bolarinwa Olaleye und Oluwapelumi Comfort Okewusi. „Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria“. Geoinformatics FCE CTU 15, Nr. 2 (08.12.2016): 59–70. http://dx.doi.org/10.14311/gi.15.2.5.
Der volle Inhalt der QuelleMartínez Prentice, Ricardo, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce und Kalev Sepp. „Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands“. Remote Sensing 13, Nr. 18 (14.09.2021): 3669. http://dx.doi.org/10.3390/rs13183669.
Der volle Inhalt der QuelleLiu, Yanzhu, Yanan Wang und Adams Wai Kin Kong. „Pixel-wise ordinal classification for salient object grading“. Image and Vision Computing 106 (Februar 2021): 104086. http://dx.doi.org/10.1016/j.imavis.2020.104086.
Der volle Inhalt der QuelleHe, Ziqiang, Shaosheng Dai und Jinsong Liu. „Single-pixel object classification using ordered illumination patterns“. Optics Communications 573 (Dezember 2024): 131023. http://dx.doi.org/10.1016/j.optcom.2024.131023.
Der volle Inhalt der QuelleKang, Min Jo, Victor Mesev und Won Kyung Kim. „Measurements of Impervious Surfaces - per-pixel, sub-pixel, and object-oriented classification -“. Korean Journal of Remote Sensing 31, Nr. 4 (31.08.2015): 303–19. http://dx.doi.org/10.7780/kjrs.2015.31.4.3.
Der volle Inhalt der QuelleDeur, Martina, Mateo Gašparović und Ivan Balenović. „An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery“. Remote Sensing 13, Nr. 10 (11.05.2021): 1868. http://dx.doi.org/10.3390/rs13101868.
Der volle Inhalt der QuelleEndo, Yutaka, und Gai Nakajima. „Compressive phase object classification using single-pixel digital holography“. Optics Express 30, Nr. 15 (15.07.2022): 28057. http://dx.doi.org/10.1364/oe.463395.
Der volle Inhalt der QuellePowar, Sudhir K., Sachin S. Panhalkar und Abhijit S. Patil. „An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques“. Geomatics and Environmental Engineering 16, Nr. 2 (09.02.2022): 61–75. http://dx.doi.org/10.7494/geom.2022.16.2.61.
Der volle Inhalt der QuelleTurissa, Pragunanti, Nababan Bisman, Siregar Vincentius, Kushardono Dony und Madduppa Hawis. „Evaluation Methods of Change Detection of Seagrass Beds in the Waters of Pajenekang and Gusung Selayar“. Trends in Sciences 18, Nr. 23 (15.11.2021): 677. http://dx.doi.org/10.48048/tis.2021.677.
Der volle Inhalt der QuelleDissertationen zum Thema "Pixel-Object classification"
Ali, Fadi. „Urban classification by pixel and object-based approaches for very high resolution imagery“. Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-23993.
Der volle Inhalt der QuellePorter, Sarah Ann. „Land cover study in Iowa: analysis of classification methodology and its impact on scale, accuracy, and landscape metrics“. Thesis, University of Iowa, 2011. https://ir.uiowa.edu/etd/1169.
Der volle Inhalt der QuelleGrift, Jeroen. „Forest Change Mapping in Southwestern Madagascar using Landsat-5 TM Imagery, 1990 –2010“. Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-22606.
Der volle Inhalt der QuelleYokum, Hannah Elizabeth. „Understanding Community and Ecophysiology of Plant Species on the Colorado Plateau“. BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/7211.
Der volle Inhalt der QuelleAbidi, Azza. „Investigating Deep Learning and Image-Encoded Time Series Approaches for Multi-Scale Remote Sensing Analysis in the context of Land Use/Land Cover Mapping“. Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS007.
Der volle Inhalt der QuelleIn this thesis, the potential of machine learning (ML) in enhancing the mapping of complex Land Use and Land Cover (LULC) patterns using Earth Observation data is explored. Traditionally, mapping methods relied on manual and time-consuming classification and interpretation of satellite images, which are susceptible to human error. However, the application of ML, particularly through neural networks, has automated and improved the classification process, resulting in more objective and accurate results. Additionally, the integration of Satellite Image Time Series(SITS) data adds a temporal dimension to spatial information, offering a dynamic view of the Earth's surface over time. This temporal information is crucial for accurate classification and informed decision-making in various applications. The precise and current LULC information derived from SITS data is essential for guiding sustainable development initiatives, resource management, and mitigating environmental risks. The LULC mapping process using ML involves data collection, preprocessing, feature extraction, and classification using various ML algorithms. Two main classification strategies for SITS data have been proposed: pixel-level and object-based approaches. While both approaches have shown effectiveness, they also pose challenges, such as the inability to capture contextual information in pixel-based approaches and the complexity of segmentation in object-based approaches.To address these challenges, this thesis aims to implement a method based on multi-scale information to perform LULC classification, coupling spectral and temporal information through a combined pixel-object methodology and applying a methodological approach to efficiently represent multivariate SITS data with the aim of reusing the large amount of research advances proposed in the field of computer vision
Lubbe, Minette. „Comparison of pixel-based and object-oriented classification approaches for detection of camouflaged objects“. Thesis, 2012. http://hdl.handle.net/10210/4455.
Der volle Inhalt der QuelleThe dissertation topic is the comparison of pixel-based and object-oriented image analysis approaches for camouflaged object detection research. A camouflage field trial experiment was conducted during 2004. For the experiment, 11 military vehicles were deployed along a tree line and in an open field. A subset of the vehicles was deployed with a variety of experimental camouflage nets and a final subset was left uncovered. The reason for deploying the camouflaged objects in the open without the use of camouflage principals was to create a baseline for future measurements. During the next experimental deployment, the camouflaged targets will be deployed according to camouflage principals. It must be emphasised that this is an experimental deployment and not an operational deployment. Unobstructed entity panels were also deployed and served as calibration entities. During the trial, both airborne (colour aerial photography) and space borne (multi-spectral QuickBird) imagery were acquired over the trial sites, and extensive calibration and ground truthing activities were conducted in support of these acquisitions. This study further describes the processing that was done after acquisition of the datasets. The goal is to determine which classification techniques are the most effective in the detection of camouflaged objects. This will also show how well or poor the SANDF camouflage nets and paint potentially perform against air and space based sensors on the one hand and classification techniques on the other. Using this information, DPSS can identify the nets and paints that need to be investigated for future enhancements (e.g. colour selection, colour combinations, base material, camouflage patterns, entity shapes, entity textures, etc.). The classification techniques to be used against SANDF camouflaged objects will also give an indication of their performance against camouflaged advesarial forces in the future.
Wang, Wenyi, und 王文宜. „A study of Region Object-oriented Classification & pixel-based Classification on the Remote Sensing image of the landslide area of Wan Dan Reservoir“. Thesis, 2011. http://ndltd.ncl.edu.tw/handle/21776349064403022439.
Der volle Inhalt der Quelle嶺東科技大學
數位媒體設計研究所
100
In the image classification, it generally used pixel-based classification model to extract information of image. The results of by pixel-based algorithm can induce Salt-and-Pepper Effect. Therefore, this study purposed a region-based model of Region Object-oriented Classification (ROC) to extract landslide image information. The surface information from the Wan Da reservoir area is collected and studied. Region Object-oriented Classification (ROC) is used to classify the landslide area. We collected different spectrum with several texture information to analyze the surrounding area of Wan Da reservoir. Entropy based classification is used as a classifier to determine the landslide/non-landslide area. Various parameters of S (similarity) and A (area) are used and then the best combinations are found. In the parallel study, we developed a pixel based classification through the calculation on the entropy for simple comparison. The relations of occurrence vs. non-occurrence of landslide with regards to attributes of land surface are studied. Thus, this could be of help to manage the recover on the landslide area.
Diyan, Mohammad Abdullah Abu. „Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh“. Master's thesis, 2011. http://hdl.handle.net/10362/5624.
Der volle Inhalt der QuelleThis study investigates the potential of using very high resolution (VHR) QuickBird data to conduct vegetation classification of the Sundarban mangrove forest in Bangladesh and compares the results with Landsat TM data. Previous studies of vegetation classification in Sundarban involved Landsat images using pixel-based methods. In this study, both pixelbased and object-based methods were used and results were compared to suggest the preferred method that may be used in Sundarban. A hybrid object-based classification method was also developed to simplify the computationally demanding object-based classification, and to provide a greater flexibility during the classification process in absence of extensive ground validation data. The relation between NDVI (Normalized Difference Vegetation Index) and canopy cover was tested in the study area to develop a method to classify canopy cover type using NDVI value. The classification process was also designed with three levels of thematic details to see how different thematic scales affect the analysis results using data of different spatial resolutions. The results show that the classification accuracy using QuickBird data stays higher than that of Landsat TM data. The difference of classification accuracy between QuickBird and Landsat TM remains low when thematic details are low, but becomes progressively pronounced when thematic details are higher. However, at the highest level of thematic details, the classification was not possible to conduct due to a lack of appropriate ground validation data.(...)
Caeiro, Ricardo Alexandre da Silva. „Classificação de dados Landsat 8 do Norte de Portugal com recurso a Geographic Object-Based Image Analysys (GEOBIA)“. Master's thesis, 2015. http://hdl.handle.net/10362/17856.
Der volle Inhalt der QuelleRemote sensing is a science and technique that allows to acquire information about physical features of an object from a particular surface, through electromagnetic radiation without any kind of physical contact with the object itself. This technique is fundamental in the field of planning and land use and also in the monitoring of the Earth’s surface, helping actors and stakeholders in the decision making process. The aim of this thesis is to classify Landsat 8 data in the north of Portugal using Geographic Object-Based Image Analysis (GEOBIA) and explore its potential and limitations in low-resolution spectral images, as in the case of the new Landsat 8 images. Subsequently, the results from the objects-oriented classification was compared to the results extracted from the classicization based on pixel by pixel and segments, in order to assess their overall accuracy and kappa index. The software’s used in this thesis were the ENVI 5.0 and the eCognition 9.0. The classification with better overall accuracy and Kappa index is the object-oriented classification, with values of 56% (14 classes) and 61% (10 classes) and 0.46 (14 classes) and 0.50 (10 classes), respectively. The classifications based on pixels and segments achieved overall accuracy values of 49% (14 classes) and 57% (10 classes), 45% (14 classes) and 52% (10 classes), respectively. For the concordance kappa index, the classifications based on pixels and segments achieved values of 0.40 (14 classes) and 0.45 (10 classes), 0.34 (14 classes) and 0.34 (10 classes). Afterwards, it was carried out a new mapping land cover, designated COS 2015, in order to find out if the classification based on objects was vulnerable due to the thematic uncertainty of the COS 2007 or by the time difference between the COS 2007 and the Landsat 8 image. Finally, the overall accuracy of the classification and the kappa index was calculated. The overall accuracy of the object-oriented classification and the COS 2007 was 59%, with a kappa index of 0.47. In the objects-oriented classification and the COS 2015, the overall accuracy was 60% and the Kappa index was 0.48.
Jung, Richard. „A multi-sensor approach for land cover classification and monitoring of tidal flats in the German Wadden Sea“. Doctoral thesis, 2016. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2016040714380.
Der volle Inhalt der QuelleBuchteile zum Thema "Pixel-Object classification"
Küppers, Fabian, Anselm Haselhoff, Jan Kronenberger und Jonas Schneider. „Confidence Calibration for Object Detection and Segmentation“. In Deep Neural Networks and Data for Automated Driving, 225–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_8.
Der volle Inhalt der QuelleMohd Zaki, Nurul Ain, Intan Nur Suhaida Mohd Radzi, Zulkiflee Abd Latif, Mohd Nazip Suratman, Mohd Zainee Zainal und Sharifah Norashikin Bohari. „Dominant Tree Species Estimation for Tropical Forest Using Pixel-Based Classification Support Vector Machine (SVM) and Object-Based Classification (OBIA)“. In Charting the Sustainable Future of ASEAN in Science and Technology, 319–33. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3434-8_28.
Der volle Inhalt der QuelleKefi, Chayma, Amina Mabrouk, Nabila Halouani und Haythem Ismail. „Comparison of Pixel-Based and Object-Oriented Classification Methods for Extracting Built-Up Areas in Coastal Zone“. In Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions (2nd Edition), 2151–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51210-1_336.
Der volle Inhalt der QuelleAli, S. S., P. M. Dare und S. D. Jones. „A Comparison of Pixel- and Object-Level Data Fusion Using Lidar and High-Resolution Imagery for Enhanced Classification“. In Lecture Notes in Geoinformation and Cartography, 3–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93962-7_1.
Der volle Inhalt der QuelleKefi, Chayma, Amina Mabrouk und Haythem Ismail. „Comparison of Pixel-Based and Object-Oriented Classification Methods for Extracting Built-Up Areas in a Coastal Zone“. In Research Developments in Geotechnics, Geo-Informatics and Remote Sensing, 335–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72896-0_76.
Der volle Inhalt der QuelleYao, Wei, und Jianwei Wu. „Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics“. In Urban Informatics, 367–400. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.
Der volle Inhalt der QuelleSampedro, Carolina, und Carlos F. Mena. „Remote Sensing of Invasive Species in the Galapagos Islands: Comparison of Pixel-Based, Principal Component, and Object-Oriented Image Classification Approaches“. In Understanding Invasive Species in the Galapagos Islands, 155–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67177-2_9.
Der volle Inhalt der QuelleSarzana, Tommaso, Antonino Maltese, Alessandra Capolupo und Eufemia Tarantino. „Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images“. In Computational Science and Its Applications – ICCSA 2020, 797–812. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58811-3_57.
Der volle Inhalt der QuelleVasavi, S., Ayesha Farha Shaik und Phani chaitanya Krishna Sunkara. „Moving Object Classification Under Illumination Changes Using Binary Descriptors“. In Optoelectronics in Machine Vision-Based Theories and Applications, 188–232. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5751-7.ch007.
Der volle Inhalt der QuelleA. Alshari, Eman, und Bharti W. Gawali. „Artificial Intelligence Techniques for Observation of Earth’s Changes“. In Altimetry - Theory, Applications and Recent Advances [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110039.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Pixel-Object classification"
Jiao, Shuming. „Fast object classification in single-pixel imaging“. In Sixth International Conference on Optical and Photonic Engineering, herausgegeben von Yingjie Yu, Chao Zuo und Kemao Qian. SPIE, 2018. http://dx.doi.org/10.1117/12.2502983.
Der volle Inhalt der QuelleWu, Chuang, Lihua Tian und Chen Li. „Pixel-wise binary classification network for salient object detection“. In Eleventh International Conference on Machine Vision, herausgegeben von Dmitry P. Nikolaev, Petia Radeva, Antanas Verikas und Jianhong Zhou. SPIE, 2019. http://dx.doi.org/10.1117/12.2523113.
Der volle Inhalt der QuelleYue, Yuanli, Shouju Liu und Chao Wang. „Reservoir computing assisted single-pixel high-throughput object classification“. In Optical Sensing and Detection VIII, herausgegeben von Francis Berghmans und Ioanna Zergioti. SPIE, 2024. http://dx.doi.org/10.1117/12.3022550.
Der volle Inhalt der QuelleRaza, Ibad-Ur-Rehman, Syed Saqib Ali Kazmi, Syed Saad Ali und Ejaz Hussain. „Comparison of Pixel-based and Object-based classification for glacier change detection“. In 2012 Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2012. http://dx.doi.org/10.1109/eorsa.2012.6261178.
Der volle Inhalt der QuelleCornic, A., K. Ose, D. Ienco, E. Barbe und R. Cresson. „Assessment of Urban Land-Cover Classification: Comparison Between Pixel and Object Scales“. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554617.
Der volle Inhalt der QuelleYounis, Mohammed Chachan, Edward Keedwell und Dragan Savic. „An Investigation of Pixel-Based and Object-Based Image Classification in Remote Sensing“. In 2018 International Conference on Advanced Science and Engineering (ICOASE). IEEE, 2018. http://dx.doi.org/10.1109/icoase.2018.8548845.
Der volle Inhalt der QuelleMuhammad, Sher, Chaman Gul, Amir Javed, Javeria Muneer und Mirza Muhammad Waqar. „Comparison of glacier change detection using pixel based and object based classification techniques“. In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723739.
Der volle Inhalt der QuelleZhang, Meng, und Liang Hong. „Deep Learning Integrated with Multiscale Pixel and Object Features for Hyperspectral Image Classification“. In 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS). IEEE, 2018. http://dx.doi.org/10.1109/prrs.2018.8486304.
Der volle Inhalt der QuelleWang, Peifa, Xuezhi Feng, Shuhe Zhao, Pengfeng Xiao und Chunyan Xu. „Comparison of object-oriented with pixel-based classification techniques on urban classification using TM and IKONOS imagery“. In Geoinformatics 2007, herausgegeben von Weimin Ju und Shuhe Zhao. SPIE, 2007. http://dx.doi.org/10.1117/12.760759.
Der volle Inhalt der QuelleZhang, Aiying, und Ping Tang. „Fusion algorithm of pixel-based and object-based classifier for remote sensing image classification“. In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723390.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Pixel-Object classification"
Vick, Tyler. Comparing Pixel- and Object-Based Classification Methods for Determining Land-Cover in the Gee Creek Watershed, Washington. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/geogmaster.22.
Der volle Inhalt der QuelleBishop, Megan, Vuong Truong, Sophia Bragdon und Jay Clausen. Comparing the thermal infrared signatures of shallow buried objects and disturbed soil. Engineer Research and Development Center (U.S.), September 2024. http://dx.doi.org/10.21079/11681/49415.
Der volle Inhalt der QuelleAhn, Yushin, und Richard Poythress. Impervious Surfaces from High Resolution Aerial Imagery: Cities in Fresno County. Mineta Transportation Institute, Mai 2024. http://dx.doi.org/10.31979/mti.2024.2257.
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