:: Volume 2, Issue 2 (9-2014) ::
jgit 2014, 2(2): 17-36 Back to browse issues page
Fuzzy Systems for Automatic Road Network Detection from High Resolution Satellite Images accentuating on Angular Texture Information
Mohammad Ali Salehi Amin *, Mehdi Mokhtarzade, Mohammad Javad Valadan Zoej
K.N.Toosi University of Technology
Abstract:   (4099 Views)
In this paper an efficient method for automatic road detection from high-resolution multi-spectral IKONOS images is presented. The system includes four main steps: In the first step the input image is segmented into road and background classes using K-means clustering and then some misclassification pixels in road binary map are removed using a median filter. In the second step, angular texture shape descriptors (mean, compactness and eccentricity) are driven for every road pixel in road binary map. In the third step, these descriptors are introduced into a fuzzy inference system. In the fuzzy system each descriptor is introduced as a linguistic variable with Gaussian membership functions while their parameters are set automatically according to statistical properties of each descriptor. Also, some fuzzy if-then rules are established. By using the centroid defuzzification, road network is distinguished from other spectrally similar classes (shadows, buildings, parking lots and etc). Then, road network is completed by connecting road pixels together and removed of small paths. In the last step of system evaluation, obtained results are compared with manually extracted road network and some accuracy assessment parameters are computed. The conventional maximum likelihood classification (MLC) is also implemented and the same accuracy assessment parameters are determined for comparison. Preliminary results show the effectiveness of the methodology of this paper in both resembling the desired results of road networks and achieving a good automation level. Furthermore, it outperforms MLC to high extent.
Keywords: K-means, Angular Texture, Fuzzy, Automatic road detection.
Full-Text [PDF 1218 kb]   (1295 Downloads)    
Type of Study: Research |
Received: 2015/09/8 | Accepted: 2015/09/8 | Published: 2015/09/8



XML   Persian Abstract   Print



Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 2, Issue 2 (9-2014) Back to browse issues page