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Fuzzy Logic System for Road Identification Using Ikonos Images
Author(s) -
Amini Jalal,
Lucas Caro,
Saradjian Mohammad,
Azizi Ali,
Sadeghian Saeed
Publication year - 2002
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/0031-868x.00201
Subject(s) - fuzzy logic , identification (biology) , pixel , artificial intelligence , gaussian , computer science , gaussian function , computer vision , satellite , standard deviation , membership function , function (biology) , scale (ratio) , geography , pattern recognition (psychology) , remote sensing , fuzzy set , cartography , mathematics , statistics , engineering , biology , botany , physics , quantum mechanics , evolutionary biology , aerospace engineering
Research into both extraction of man‐made objects and automatic change detection from aerial and satellite images has made significant progress in recent years. This paper presents an approach, based on a fuzzy logic system, for the identification of suburban roads in Ikonos images. The linguistic variables are the mean and standard deviation (SD) of different objects with Gaussian membership function. After the roads have been identified provisionally and their skeleton extracted, the skeleton can be vectorised and then used as direct input to a GIS for further analysis. The method was tested on an Ikonos “Geo” image covering Bilesavar in north‐western Iran. For the suburban area of Bilesavar, the results showed that grey scale values ranged from 20 to 190 for non‐roads and 226 to 228 for roads, the optimum width of the Gaussian kernel function was 3 and the SD was 0.4. It was also found that about 91% of main roads with a width of 6 to 12 pixels could be extracted from high resolution satellite imagery by the algorithm. The computer program for this study has been developed in visual C++ based on Windows 98