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Abstrakty
In this paper, we will show the capabilities and limitations of Alsat-2 images in mapping urban areas in emergency situation. The aim of the research was to provide urban information that is geo-referenced in real time during natural disasters (floods, earthquakes). It’s important for fast decision-making so that they will be a necessary support for the estimation of the damages. The following study tests the spatial and radiometric quality of Alsat 2-A images and proposes technical solutions for theiruse in urban mapping. In order to identify and extract the ground realities, we shall describe and make an effort to discern the perceptible aspects of features in urban area. The adopted methodology carries out a statistical analysis of the information extracted from Alsat-2 images of the studied area (the city of M’Sila, Algeria) using classification and segmentation methods. The statistics will show the percentage of the area in relation to the total size of geometric surface and the distance for linear objects. As a result, the quality of the extracted urban texture necessary for urban mapping will be determined. Image processing to improve resolution quality was carried out using merging method. However, the analysis of consistency and discrepancy of these statistics will be done by comparing samples of field data using confusion matrix.
Czasopismo
Rocznik
Tom
Strony
109--122
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
autor
- University of M’Sila, Algeria Laboratory Smart City, Geomatics and Gouvernance
autor
- University of M’Sila, Algeria Faculty of Math and Computer Sciences
Bibliografia
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- Benza M., Weeks J.R., Stow D.A., López-Carr D., Clarke K.C. 2016. A pattern-based definition of urban context using remote sensing and GIS. Remote Sensing of Environment, 183, 250–264. https://doi.org/10.1016/j.rse.2016.06.011
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- Haddanou M. 2005. Les spécifications cartographiques en usage à l’INCT. Le bulletin interne de l’Institut National de Cartographie et de Télédétection, édition spéciale, 3, 15.
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- Hrvatin M., Perko D. 2003. Surface roughness and land use in Slovenia/Razgibanost površja in raba tal v Sloveniji. Acta Geographica Slovenica, 43-2. DOI: https://doi.org/10.3986/ AGS43202
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- Leichtle T., Geiß C., Wurm M., Lakes T., Taubenböck H. 2017. Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54, 15–27. https://doi.org/10.1016/j.jag.2016.08.010
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- Pesaresi M., Benediktsson J.A. 2001. A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 309–320. https://doi.org/10.1109/36.905239
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- Shan Yu, Berthod M., Giraudon G. 1999. Toward robust analysis of satellite images using map information-application to urban area detection. IEEE Transactions on Geoscience and Remote Sensing, 37(4), 1925–1939. https://doi.org/10.1109/36.774705
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- Singh P.P, Garg R.D. 2012. Automatic Road Extraction from High Resolution Satellite Image using Adaptive Global Thresholding and Morphological Operations. Journal of the Indian Society of Remote Sensing, 41. https://doi.org/10.1007/s12524-012-0241-4
- Song J., Gao S., Zhu Y., Ma C. 2019. A survey of remote sensing image classification based on CNNs. Big Earth Data, 3(3), 232–254. https://doi.org/10.1080/20964471.2019.1657720
- Sparfel L., Gourmelon F., Le Berre I. 2008. Approche orientée-objet de l’occupation des sols enzone côtière. Revue Télédétection, 8, 4, 237–256.
- Taubenböck H., Standfuß I., Wurm M., Krehl A., Siedentop S. 2017. Measuring morphological polycentricity. A comparative analysis of urban mass concentrations using remote sensing data. Computers, Environment and Urban Systems, 64, 42–56. https://doi.org/10.1016/j. compenvurbsys. 01.005 UNSD – Welcome to UNSD. Consulté 1 mars 2022. https://unstats.un.org/home/
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- Van der Linden S., Okujeni A., Canters F., Degerickx J., Heiden U., Hostert P., Priem F., Somers B., Thiel F. 2019. Imaging Spectroscopy of Urban Environments. Surveys in Geophysics, 40(3), 471–488. https://doi.org/10.1007/s10712-018-9486-y
- Wang L., Shi C., Diao C., Ji W., Yin D. 2016. A survey of methods incorporating spatial information in image classification and spectral unmixing. International Journal of Remote Sensing, 37(16), 3870–3910. https://doi.org/10.1080/01431161.2016.1204032
- Weng Q. 2012. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment, 117, 34–49. https://doi.org/10.1016/j.rse.2011.02.030
- Zhang R., Lin X. 2010. Automatic road extraction based on local histogram and support vector data description classifier from very high resolution digital aerial. 2010 IEEE International Geoscience and Remote Sensing Symposium, 441–444. https://doi.org/10.1109/IGARSS.2010.5654117
- Zhu Q., Zhong Y., Liu Y., Zhang L., Li D. 2018. A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification. Remote Sensing, 10. https://doi.org/10.3390/rs10040568
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cc5efc23-4d53-4b4d-a3ad-378e2af23628