Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 06 |

Tytuł artykułu

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)

Treść / Zawartość

Warianty tytułu

Języki publikacji



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.






Opis fizyczny



  • Department of Geography, Payame Noor University, PO BOX 19395 - 3697, Tehran, Iran
  • Department of Geographic Information Systems (GIS), Shahihd Beheshti University, Tehran, Iran
  • Department of Geography, Payame Noor University, PO BOX 19395 - 3697, Tehran, Iran
  • Department of Geography, Payame Noor University, PO BOX 19395 - 3697, Tehran, Iran


  • [1] Japanese remote sensing association (1993). Remote sensing basics, trans, by Jahedi,Farshid and Farokhi, Shahrokh, (First version, Iranian remote sensing Publications).
  • [2] Pashazadee, Gholamhussein (2009). Comparing the pixel-based and object-oriented methods in classifying the urban land use, using the remote sensing data (Case study, Urmia), M.A thesis, Geology sciences faculty, Shahid beheshti university, Tehran
  • [3] Zobeiri Mahmood, Majd Alireza (2003). Familiarity with the remote sensing technique and its application in the natural sources(satellite information, spac nd arial images), Fourth version, Tehran university publications.
  • [4] Abdi Parviz (2005). Identification and evaluation of forest lands, using RS-GIS in Zanjan, Geomatic conference in geology, Second version, Tehran university publications), The application of remote sensing in geology, Second version, Tehran university publications.
  • [5] Alavipanah Seid Kazem (2006). The application of remote sensing in geology, second version, Tehran university publications.
  • [6] Alavipanah, Seid Kazem, Matinfar Hamidreza, Rafieemam Ammar (2008). The application of IT in geology, Radar, supra and multiple spectral remote sensing statistics earth, First version, Geographic information systems, Nervous networks, phase sets an statistics earth, Tehran university publications.
  • [7] Fatemi Seidbagher; Rezae Yousef (2008). Remote sensing basics, Azade publications.
  • [8] Karami, Jalal (2003), Objects – oriented classification of Land sat ETM+ OF Malayer region based on size in the artificial nervous networks, M.A. thesis, Humanities Faculty, Tarbyt Modaraes University.
  • [9] Mahmoodzade Hasan (2004). The application of multi temporal satellite data in the GIS with the aim of investigating land use changes of Tabriz, M.A. thesis, Humanities Faculty, Tabriz University.
  • [10] Congalton R. G., Green K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition, CRC Press, Taylor & Francis Group.
  • [11] Gao J. (2009). Digital Analysis of Remote Sensing Imagery, McGraw-Hill Companies, Inc.
  • [12] Herold M., Scepan J. (2002). Object-oriented mapping and analysis of urban land use/cover using IKONOS data, Proceedings of 22nd EARSEL Symposium “Geoinformation for European-wide integration”, Prague.
  • [13] Janssen L. L. F. (2001). Principles of Remote Sensing An introductory textbook, Second Edition, ITC Educational Textbook Series, Enschede, The Netherlands.
  • [14] Lillesand T. M., Kiefer R. W., Chipman J. W. (2004). Remote Sensing and Image Interpretation, fifth Edition, John Wiley & Sons, Inc.
  • [15] Lutfi Suzen M. (2002). Data Driven Landslide Hazard Assessment Using Geographical Information Systems and Remote Sensing, Doctor Thesis, Department Of Geological Engineering, School Of Natural And Applied Sciences, Middle East Technical University, Turkey.
  • [16] Matinfar R., Sarmadian F., Alavi Panah S. K., Heck R. American-Eurasian J. Agric. & Environ. Sci. 2(4) (2007) 448-456.
  • [17] Navulur K. (2007). Multispectral Image Analysis Using the Object-Oriented Paradigm, CRC Press Taylor & Francis Group.
  • [18] Qian, J., Zhoua Q., Houa Q. (2007). Comparison Of Pixel-Based And Object-Oriented Classification Methods For Extracting Built-Up Areas In Arid zone, ISPRS Workshop on Updating Geo-spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs
  • [19] Richards J. A., Jia X. (2006). Remote Sensing Digital Image Analysis An Introduction, 4th Edition, Springer-Verlag Berlin Heidelberg.
  • [20] Taubenböck H., Esch T., Roth A. (2006). An Urban Classification Approach Based On An Object-Oriented Analysis Of High Resolution Satellite Imagery For A Spatial Structuring Within Urban Areas, 1st EARSeL Workshop of the SIG Urban Remote Sensing, Humboldt-Universität zu Berlin.
  • [21] Varshney P. K., Arora M. K. (2004). Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, Springer Verlag, Berlin.

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.