Road Extraction Based on Direction Consistency Segmentation

A common strategy for road extraction from remote sensing images is classification based on spectral information. However, due to a common phenomenon that different objects can be with similar spectral characteristics, classification results usually contain many interference regions which do not correspond to any road entity. To solve this problem, a road extraction method based on direction consistency segmentation is proposed in this paper. In binary road classification images, considering that road regions in these images usually have consistent local directions, pixels with similar main directions are merged into objects. After acquiring these objects, geometric measurements such as LFI (Linear Feature Index) and region area are calculated and a segment-linking algorithm is used to recognize and extract road objects among them. Various test images are used to verify the effectiveness of this method and contrast experiments are performed between the proposed binary image processing method and two existing methods. Experimental results show that this method has advantages in both accuracy, computational efficiency and stability, which can be used to extract road regions in remote sensing images at different resolutions.

[1]  Aleksandra Pizurica,et al.  Edge Linking Based Method to Detect and Separate Individual C. Elegans Worms in Culture , 2008, 2008 Digital Image Computing: Techniques and Applications.

[2]  Xin Huang,et al.  Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines , 2009 .

[3]  Wenzhong Shi,et al.  Road Centerline Extraction From High-Resolution Imagery Based on Shape Features and Multivariate Adaptive Regression Splines , 2013, IEEE Geoscience and Remote Sensing Letters.

[4]  LI Hai-tao.  Semi-automatic Extraction of Ribbon Roads from High Resolution Remotely Sensed Imagery Based on Angular Texture Signature and Profile Match , 2008 .

[5]  Wang Weixing,et al.  A Method of Road Extraction from High-resolution Remote Sensing Images Based on Shape Features , 2009 .

[6]  Yang Jinghui.  Semi-automatic Extraction of Ribbon Road from High Resolution Remotely Sensed Imagery by a T-shaped Template Matching , 2009 .

[7]  D. Civco,et al.  Road Extraction Using SVM and Image Segmentation , 2004 .

[8]  Qiaoping Zhang,et al.  Benefit of the angular texture signature for the separation of parking lots and roads on high resolution multi-spectral imagery , 2006, Pattern Recognit. Lett..

[9]  Donna Haverkamp.  Extracting straight road structure in urban environments using IKONOS satellite imagery , 2002 .

[10]  Steven W. Zucker,et al.  Region growing: Childhood and adolescence* , 1976 .

[11]  Jia Cheng-li.  Automatic Road Extraction from SAR Imagery Based on Genetic Algorithm , 2008 .

[12]  Sukhendu Das,et al.  Use of Salient Features for the Design of a Multistage Framework to Extract Roads From High-Resolution Multispectral Satellite Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.