Performance of Supervised Classification on Landsat images for Mapping Land Cover in Hour AL-Dalmaj Area

Zaydan Ali, Hussain

	Performance of Supervised Classification on Landsat images for Mapping Land Cover in Hour AL-Dalmaj Area
Al-Mustansiriyah Journal of Science
Article 1, Volume 26, Issue 2, 2015, Pages 74-77
Author
Hussain Zaydan Ali
Abstract
Wetlands are highly valued for conservation. Remote sensing is required especially monitoring and classification to wetlands inventory, due to declination of general properties. The production of thematic maps, such as those depicting land cover, using an image classification is one of the most common applications of remote sensing. Several methods exist for remote sensing image classification; they include supervised, unsupervised and other approaches. Accuracy assessment of a remote sensing output is a most important step in classification of remotely sensed data, and without accuracy assessment the quality of map or output produced would be of lesser value to the end user. This paper perform supervised classification technique on remote sensing data for land cover classification in Hour AL-Dalmaj area and evaluate the accuracy result of classification technique using ERDAS IMAGINE V9.2. The study used Landsat 7 satellite image as a primary data. The land cover classes for the study area were classified into five themes. A total of 93 sample points were collected using equalized Random Sampling. The results showed that the overall accuracy for the supervised classification was 83.87 %, where Kappa statistics was 0.7977.

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