Using Geographic Information Systems in Remotely Sensed Data Classification (Comparison Study)

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Mustafa Abudula Alsuidi
Ali Kareem Mohemad Al-Nasrawi
Hodoud Mohemad Abood AL-Toffalee

Abstract

Remote sensing datasets provide a massive amount of information that is difficult to deal with using traditional methods. Thus، modern techniques are necessary to be usedwith such information and data. Toolsets provided by GIS to management and analysis this data to gain better results to be used in various fields of planning، research and others.


 This study is compering between the classification methods of remotely sensed data. There are severalways of classifications that can be applied using GIS، where the study dealt with the main methods of digital classification (both methods:  "supervised and unsupervised)and visual classification.


 The results showed that each method was characterized by some certain advantages making it better than other methods in some aspects، as the numerical classification is more accurate in distinguishing large-scale phenomena. Whereas، the visual classification is having a better accuracy in identifying small-scale phenomena.


 The study also found the possibility of using the Arc GIS software to manage and display the results of the two classifications، as well as the possibilities of controlling the color ramp of the items resulting from the two classifications process to distinguish them، as well as conducting measurements and other cartographic treatments and outputting the results by various cartographic methods from maps، reports، tables and graphs.

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How to Cite
[1]
M. A. . Alsuidi, A. K. . M. . . Al-Nasrawi, and H. M. . A. . . AL-Toffalee, “Using Geographic Information Systems in Remotely Sensed Data Classification (Comparison Study)”, JUBH, vol. 28, no. 6, pp. 74-96, Sep. 2020.
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