Author(s)

Abdlhamed Jamil^*, Abdulmohsen Al-Shareef, Amer Al-Thubaiti

Department of Geography & GIS, Faculty of Arts & Humanities, King Abdulaziz University, Jeddah, KSA.

This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.

Remote Sensing, Satellite Imagery, Image Processing, Classification, Assessment, Urban

Jamil, A. , Al-Shareef, A. and Al-Thubaiti, A. (2020) Classifications of Satellite Imagery for Identifying Urban Area Structures. Advances in Remote Sensing, 9, 12-32. doi: 10.4236/ars.2020.91002.