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)

• Autorzy: Safaralizade E. , Husseinzade R. , Pashazade G. , Khosravi B.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

pixel-based algorithm; urban land; land use; multispectral image; IKONOS satellite; classification; urbanization; urban planning; Uromia city

• Wydawca: -
• Czasopismo: International Letters of Natural Sciences
• Rocznik: 2014
• Tom: 06
• Opis fizyczny: p.40-56,fig.,ref.

Twórcy

autor

Safaralizade E.

• Department of Geography, Payame Noor University, PO BOX 19395 - 3697, Tehran, Iran

autor

Husseinzade R.

• Department of Geographic Information Systems (GIS), Shahihd Beheshti University, Tehran, Iran

autor

Pashazade G.

• Department of Geography, Payame Noor University, PO BOX 19395 - 3697, Tehran, Iran

autor

Khosravi B.

• Department of Geography, Payame Noor University, PO BOX 19395 - 3697, Tehran, Iran

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