Gotowe bibliografie tematyczne / Pixel-Object classification

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji
  5. Raporty organizacyjne

Gotowa bibliografia na temat „Pixel-Object classification”

Autor: Grafiati
Data publikacji: 5 października 2024
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Pixel-Object classification"

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Bernardini, A., E. Frontoni, E. S. Malinverni, A. Mancini, A. N. Tassetti, and P. Zingaretti. "Pixel, object and hybrid classification comparisons." Journal of Spatial Science 55, no. 1 (2010): 43–54. http://dx.doi.org/10.1080/14498596.2010.487641.

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Makinde, Esther Oluwafunmilayo, Ayobami Taofeek Salami, James Bolarinwa Olaleye, and Oluwapelumi Comfort Okewusi. "Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria." Geoinformatics FCE CTU 15, no. 2 (2016): 59–70. http://dx.doi.org/10.14311/gi.15.2.5.

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Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based
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Martínez Prentice, Ricardo, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp. "Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands." Remote Sensing 13, no. 18 (2021): 3669. http://dx.doi.org/10.3390/rs13183669.

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High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs be
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Sutanto, Ahmad, Bambang Trisakti, and Aniati Murni Arymurthy. "PERBANDINGAN KLASIFIKASI BERBASIS OBJEK DAN KLASIFIKASI BERBASIS PIKSEL PADA DATA CITRA SATELIT SYNTHETIC APERTURE RADAR UNTUK PEMETAAN LAHAN." Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital 11, no. 1 (2014): 63–75. https://doi.org/10.30536/inderaja.v11i1.3300.

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Utilization of remote sensing data for land mapping has long been developed. In Indonesia, as a tropical region, the cloud becomes a classic problem in observing the Earth’s surface using optical remotely sensor satellite. Synthetic Aperture Radar (SAR) sensor satellite has the ability to penetrate clouds so it can solve cloud cover problems. In this study, the ALOS PALSAR data were used to assess object-based and pixel-based classification techniques. This data was chosen due to its capacity for object recognition based on backscatter characteristics. Object-based classification using the met
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He, Ziqiang, Shaosheng Dai, and Jinsong Liu. "Single-pixel object classification using ordered illumination patterns." Optics Communications 573 (December 2024): 131023. http://dx.doi.org/10.1016/j.optcom.2024.131023.

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Liu, Yanzhu, Yanan Wang, and Adams Wai Kin Kong. "Pixel-wise ordinal classification for salient object grading." Image and Vision Computing 106 (February 2021): 104086. http://dx.doi.org/10.1016/j.imavis.2020.104086.

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Deur, Martina, Mateo Gašparović, and 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, no. 10 (2021): 1868. http://dx.doi.org/10.3390/rs13101868.

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Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three diffe
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Kang, Min Jo, Victor Mesev, and Won Kyung Kim. "Measurements of Impervious Surfaces - per-pixel, sub-pixel, and object-oriented classification -." Korean Journal of Remote Sensing 31, no. 4 (2015): 303–19. http://dx.doi.org/10.7780/kjrs.2015.31.4.3.

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Endo, Yutaka, and Gai Nakajima. "Compressive phase object classification using single-pixel digital holography." Optics Express 30, no. 15 (2022): 28057. http://dx.doi.org/10.1364/oe.463395.

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A single-pixel camera (SPC) is a computational imaging system that obtains compressed signals of a target scene using a single-pixel detector. The compressed signals can be directly used for image classification, thereby bypassing image reconstruction, which is computationally intensive and requires a high measurement rate. Here, we extend this direct inference to phase object classification using single-pixel digital holography (SPDH). Our method obtains compressed measurements of target complex amplitudes using SPDH and trains a classifier using those measurements for phase object classifica
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Powar, Sudhir K., Sachin S. Panhalkar, and 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, no. 2 (2022): 61–75. http://dx.doi.org/10.7494/geom.2022.16.2.61.

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Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, t
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Rozprawy doktorskie na temat "Pixel-Object classification"

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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.

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Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV i
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Porter, 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.

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For landscapes dominated by agriculture, land cover plays an important role in the balance between anthropogenic and natural forces. Therefore, the objective of this thesis is to describe two different methodologies that have been implemented to create high-resolution land cover classifications in a dominant agricultural landscape. First, an object-based segmentation approach will be presented, which was applied to historic, high resolution, panchromatic aerial photography. Second, a traditional per-pixel technique was applied to multi-temporal, multispectral, high resolution aerial photograph
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Grift, 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.

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The main goal of this study was to map and measure forest change in the southwestern part of Madagascar near the city of Toliara in the period 1990-2010. Recent studies show that forest change in Madagascar on a regional scale does not only deal with forest loss, but also with forest growth However, it is unclear how the study area is dealing with these patterns. In order to select the right classification method, pixel-based classification was compared with object-based classification. The results of this study shows that the object-based classification method was the most suitable method for
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Yokum, Hannah Elizabeth. "Understanding Community and Ecophysiology of Plant Species on the Colorado Plateau." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/7211.

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The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. T
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Abidi, 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.

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Cette thèse explore le potentiel de l'apprentissage automatique pour améliorer la cartographie de modèles complexes d'utilisation des sols et de la couverture terrestre à l'aide de données d'observation de la Terre. Traditionnellement, les méthodes de cartographie reposent sur la classification et l'interprétation manuelles des images satellites, qui sont sujettes à l'erreur humaine. Cependant, l'application de l'apprentissage automatique, en particulier par le biais des réseaux neuronaux, a automatisé et amélioré le processus de classification, ce qui a permis d'obtenir des résultats plus obj
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Lubbe, Minette. "Comparison of pixel-based and object-oriented classification approaches for detection of camouflaged objects." Thesis, 2012. http://hdl.handle.net/10210/4455.

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M.A.<br>The 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 ne
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Wang, Wenyi, and 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.

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碩士<br>嶺東科技大學<br>數位媒體設計研究所<br>100<br>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 ana
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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.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.<br>This 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
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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.

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A deteção remota é uma ciência e técnica, que permite recolher informação de características físicas de um objeto de uma determinada superfície através da radiação eletromagnética, sem entrar em contato com ela. É muito importante na área do planeamento e ordenamento do território e na monitorização da superfície terrestre, ajudando os atores e intervenientes do território no apoio à decisão. A presente dissertação tem como principal objetivo a classificação de dados Landsat 8 para o Norte de Portugal com recurso a Geographic Object-Based Image Analisys (GEOBIA) e explorar as suas potencialid
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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.

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Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse.
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Części książek na temat "Pixel-Object classification"

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Küppers, Fabian, Anselm Haselhoff, Jan Kronenberger, and Jonas Schneider. "Confidence Calibration for Object Detection and Segmentation." In Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_8.

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AbstractCalibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-kn
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Mohd Zaki, Nurul Ain, Intan Nur Suhaida Mohd Radzi, Zulkiflee Abd Latif, Mohd Nazip Suratman, Mohd Zainee Zainal, and 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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3434-8_28.

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Kefi, Chayma, Amina Mabrouk, Nabila Halouani, and 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). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51210-1_336.

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Ali, S. S., P. M. Dare, and 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. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93962-7_1.

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Kefi, Chayma, Amina Mabrouk, and 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. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72896-0_76.

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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.

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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cu
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Sampedro, Carolina, and 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. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67177-2_9.

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Sarzana, Tommaso, Antonino Maltese, Alessandra Capolupo, and 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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58811-3_57.

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Kesgin B., Esbah H., and Kurucu Y. "Comparison of pixel-based and object-based classification methods in detecting land use/land cover dynamics." In Remote Sensing for a Changing Europe. IOS Press, 2009. https://doi.org/10.3233/978-1-58603-986-8-173.

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Due to the complex spatial structure of the earth surface, obtaining a detailed and accurate land use/land cover (LULC) classification results with satellite data have still been problematic. The overall goal of this research is to compare the pixel based and object oriented image classification approaches in terms of the overall accuracies and robustness of the final classification product. An Aster image, dated 4/27/2005, with 3 bands from spectral regions of VNIR is used to perform the LULC classification for 16 different LULC classes. Ground truth data are collected from field surveys, ava
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Elatawneh A., Manakos I., Kalaitzidis C., and Schneider T. "Land-Cover Classification and Unmixing of Hyperion Image in Area of Anopoli." In Imagin[e,g] Europe. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-494-8-111.

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The Hyperion sensor is unique because it is the only high spectral resolution hyperspectral sensor on a satellite platform. The hyperspectral capability of the sensor could potentially allow for classifications of increased accuracy, in comparison to those produced from multispectral data. This study evaluates the capability of Hyperion data for discriminating land-cover classes in the Anopoli region (southwest Crete, Greece), through different classification techniques and spectral unmixing procedure. Preprocessing of Hyperion data is essential before any analysis takes place, and it includes
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Streszczenia konferencji na temat "Pixel-Object classification"

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Cheng, Yin, Yusen Liao, Xin Sun, and Jun Ke. "Optimizing object classification with single-pixel imaging and transformer networks." In Fourth International Conference on Computational Imaging (CITA 2024), edited by Xiaopeng Shao. SPIE, 2025. https://doi.org/10.1117/12.3057353.

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Jiao, Shuming. "Fast object classification in single-pixel imaging." In Sixth International Conference on Optical and Photonic Engineering, edited by Yingjie Yu, Chao Zuo, and Kemao Qian. SPIE, 2018. http://dx.doi.org/10.1117/12.2502983.

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Wu, Chuang, Lihua Tian, and Chen Li. "Pixel-wise binary classification network for salient object detection." In Eleventh International Conference on Machine Vision, edited by Dmitry P. Nikolaev, Petia Radeva, Antanas Verikas, and Jianhong Zhou. SPIE, 2019. http://dx.doi.org/10.1117/12.2523113.

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Yue, Yuanli, Shouju Liu, and Chao Wang. "Reservoir computing assisted single-pixel high-throughput object classification." In Optical Sensing and Detection VIII, edited by Francis Berghmans and Ioanna Zergioti. SPIE, 2024. http://dx.doi.org/10.1117/12.3022550.

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Raza, Ibad-Ur-Rehman, Syed Saqib Ali Kazmi, Syed Saad Ali, and 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.

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Cornic, A., K. Ose, D. Ienco, E. Barbe, and 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.

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Younis, Mohammed Chachan, Edward Keedwell, and 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.

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Muhammad, Sher, Chaman Gul, Amir Javed, Javeria Muneer, and 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.

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Zhang, Meng, and 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.

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Wang, Peifa, Xuezhi Feng, Shuhe Zhao, Pengfeng Xiao, and Chunyan Xu. "Comparison of object-oriented with pixel-based classification techniques on urban classification using TM and IKONOS imagery." In Geoinformatics 2007, edited by Weimin Ju and Shuhe Zhao. SPIE, 2007. http://dx.doi.org/10.1117/12.760759.

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Raporty organizacyjne na temat "Pixel-Object classification"

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Vick, Tyler. Comparing Pixel- and Object-Based Classification Methods for Determining Land-Cover in the Gee Creek Watershed, Washington. Portland State University Library, 2000. http://dx.doi.org/10.15760/geogmaster.22.

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Bishop, Megan, Vuong Truong, Sophia Bragdon, and Jay Clausen. Comparing the thermal infrared signatures of shallow buried objects and disturbed soil. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49415.

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The alteration of physical and thermal properties of native soil during object burial produces a signature that can be detected using thermal infrared (IR) imagery. This study explores the thermal signature of disturbed soil compared to buried objects of different compositions (e.g., metal and plastic) buried 5 cm below ground surface (bgs) to better understand the mechanisms by which soil disturbance can impact the performance of aided target detection and recognition (AiTD/R). IR imagery recorded every five minutes were coupled with meteorological data recorded on 15-minute intervals from 1
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Ahn, Yushin, and Richard Poythress. Impervious Surfaces from High Resolution Aerial Imagery: Cities in Fresno County. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2257.

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This study investigates impervious surfaces — areas covered by materials with restricted water permeability, such as pavement, sidewalks, and parking lots—due to their crucial role in influencing water dynamics within urban landscapes. The impermeability of these surfaces disrupts natural water absorption processes, resulting in adverse environmental consequences such as increased flooding, erosion, and water pollution. The research employs impervious surface analysis, a method involving the mapping and analysis of these surfaces within specified study areas, including cities, counties, and ce
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