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https://doi.org/10.1007/978-3-540-69417-5_8

has been cited by the following article:

• TITLE: Classifications of Satellite Imagery for Identifying Urban Area Structures

  AUTHORS: Abdlhamed Jamil, Abdulmohsen Al-Shareef, Amer Al-Thubaiti

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

  JOURNAL NAME: Advances in Remote Sensing, Vol.9 No.1, March 31, 2020

  ABSTRACT: 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.