Using shape extraction to enhance classification of Landsat satellite images to visualize vegetation

Abou-Shaara, Hossam F. and Kelany, Mahmoud M. (2020) Using shape extraction to enhance classification of Landsat satellite images to visualize vegetation. AGRÁRINFORMATIKA / JOURNAL OF AGRICULTURAL INFORMATICS, 11 (1). pp. 1-8. ISSN 2061-862X

Preview

Text document (37).pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Identifying vegetation from satellite images is very necessary for studies related to agricultural sector. Enhancing the quality and classification of satellite images is a challenge especially when study areas contain complex vegetation. The study aimed to enhance the image classification based on using extraction tool of the ArcGIS. In the present study, the classification of Landsat satellite images before and after extracting specific areas covering vegetation using the ArcGIS was compared. Two regions in Egypt, one with complex vegetation located at Siwa Oasis and the other one with simple vegetation located at Abu Simbel region, were used in this study. Then, several polygons were used to extract specific areas covering vegetation from the satellite images. Images were subsequently classified using Iso cluster unsupervised classification tool. The classification outcomes were compared between the original images before extraction with images after extraction. The results showed that extraction tool greatly enhanced the quality and classification of the image and it was possible to identify different types of vegetation.

Actions (login required)

Edit Item