ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLII-4-W18-665-2019 ROAD RECOGNITION BASED ON DECISION LEVEL FUSION OF SAR AND OPTIC DATA Lazari Zare M. 1 Tabib Mahmoudi F. 1 Dept. of Geomatic Engineering, Civil Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran 18 10 2019 XLII-4/W18 665 669 Copyright: © 2019 M. Lazari Zare 2019 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLII-4-W18/665/2019/isprs-archives-XLII-4-W18-665-2019.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-4-W18/665/2019/isprs-archives-XLII-4-W18-665-2019.pdf

Road recognition and extraction based on remotely sensed data is efficient and applicable in much urban management studies. In this research, the capabilities of SPOT and SAR images are investigated for road recognition. Spectral and textural similarities between roads and other urban objects such as building’s roofs many cause some difficulties in road recognition based on SPOT image. On the other hand, SAR images are good for small road recognition but, may have some difficulties for detecting roads among vegetation. The proposed method in this paper is a decision level fusion of SPOT and SAR classification results in order to modify extracted road regions. This method has three main steps; 1) texture feature extraction from each of the SPOT and SAR images, 2) classifying each of the SPOT and SAR images based on SVM classifier, 3) decision level fusion of classification results in order to reduce road recognition difficulties and having optimum road regions. Performing the capabilities of the proposed decision level fusion algorithm for road recognition can improve the quality of the classification for about 21%.