%0 Journal Article
%T Classifications of Satellite Imagery for Identifying Urban Area Structures
%A Abdlhamed Jamil
%A Abdulmohsen Al-Shareef
%A Amer Al-Thubaiti
%J Advances in Remote Sensing
%P 12-32
%@ 2169-2688
%D 2020
%I Scientific Research Publishing
%R 10.4236/ars.2020.91002
%X 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.
%K Remote Sensing
%K Satellite Imagery
%K Image Processing
%K Classification
%K Assessment
%K Urban
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=99244