Fuzzy B-spline optimization for urban slum three-dimensional reconstruction using ENVISAT satellite data

7th IGRSM International Remote Sensing & GIS Conference and Exhibition

地球科学;计算机科学

Marghany, Maged^1

Institute of Geospatial Science and Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia^1

关键词: Automatic Detection;    Automatic detection algorithms;    Critical challenges;    Developed countries;    ENVISAT satellite;    Surrounding environment;    Three-dimensional reconstruction;    Threedimensional (3-d);   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/20/1/012036/pdf
DOI  :  10.1088/1755-1315/20/1/012036

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

A critical challenges in urban aeras is slums. In fact, they are considered a source of crime and disease due to poor-quality housing, unsanitary conditions, poor infrastructures and occupancy security. The poor in the dense urban slums are the most vulnerable to infection due to (i) inadequate and restricted access to safety, drinking water and sufficient quantities of water for personal hygiene; (ii) the lack of removal and treatment of excreta; and (iii) the lack of removal of solid waste. This study aims to investigate the capability of ENVISAT ASAR satellite and Google Earth data for three-dimensional (3-D) slum urban reconstruction in developed countries such as Egypt. The main objective of this work is to utilize some 3-D automatic detection algorithm for urban slum in ENVISAT ASAR and Google Erath images were acquired in Cairo, Egypt using Fuzzy B-spline algorithm. The results show that the fuzzy algorithm is the best indicator for chaotic urban slum as it can discriminate between them from its surrounding environment. The combination of Fuzzy and B-spline then used to reconstruct 3-D of urban slum. The results show that urban slums, road network, and infrastructures are perfectly discriminated. It can therefore be concluded that the fuzzy algorithm is an appropriate algorithm for chaotic urban slum automatic detection in ENVSIAT ASAR and Google Earth data.