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Library Assessing the Accuracy of the Pixel-Based Algorithms in Classifying the Urban Land Use, Using the Multi Spectral Image of the IKONOS Satellite (Case Study, Uromia City)

Assessing the Accuracy of the Pixel-Based Algorithms in Classifying the Urban Land Use, Using the Multi Spectral Image of the IKONOS Satellite (Case Study, Uromia City)

Assessing the Accuracy of the Pixel-Based Algorithms in Classifying the Urban Land Use, Using the Multi Spectral Image of the IKONOS Satellite (Case Study, Uromia City)

Resource information

Date of publication
декабря 2014
ISBN / Resource ID
AGRIS:CH2018300038

With the development of urbanization and expansion of urban land use, the need to up to date maps, has drawn the attention of the urban planners. With the advancement of the remote sensing technology and accessibility to images with high resolution powers, the classification of these land uses could be executed in different ways. In the current research, different algorithms for classifying the pixel-based were tested on the land use of the city of Urmia, using the multi spectral images of the IKONOS satellite. Here, in this method, the algorithms of the supervised classification of the maximum likelihood, minimum distance to mean and parallel piped were executed on seven land use classes. Results obtained using the error matrix indicated that the algorithm for classifying the maximum likelihood has an overall accuracy of 88/93 % and the Kappa coefficient of 0/86 while for the algorithms of minimum distance to mean and parallel piped , the overall accuracy are 05/79 % and 40/70 % respectively. Also, the accuracy of the producer and that of the user in most land use classes in the method of maximum likelihood are higher compared to the other algorithms.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Safaralizade, Esmaeel
Husseinzade, Robab
Pashazade, Gholamhussein
Khosravi, Bakhtiar

Publisher(s)
Data Provider