The importance of acquisition of road information has recently been increased with a rapid growth of spatial-related services such as urban information system and location based service. This paper proposes an automatic road extraction method using object-based approach which was issued alternative of pixel-based method recently. Firstly, the spatial objects were created by MSRS(Modified Seeded Region Growing) method, and then the key road objects were extracted by using properties of objects such as their shape feature information and adjacency. The omitted road objects were also traced considering spatial correlation between extracted road and their neighboring objects. In the end, the final road region was extracted by connecting discontinuous road sections and improving road surfaces through their geometric properties. To assess the proposed method, quantitative analysis was carried out. From the experiments, the proposed method generally showed high road detection accuracy and had a great potential for the road extraction from high resolution satellite images.
[1]
Paolo Gamba,et al.
Road Network Extraction in VHR SAR Images of Urban and Suburban Areas by Means of Class-Aided Feature-Level Fusion
,
2010,
IEEE Transactions on Geoscience and Remote Sensing.
[2]
L. Anselin.
Local Indicators of Spatial Association—LISA
,
2010
.
[3]
Paolo Gamba,et al.
Junction-aware extraction and regularization of urban road networks in high-resolution SAR images
,
2006,
IEEE Transactions on Geoscience and Remote Sensing.
[4]
D. Civco,et al.
Road Extraction Using SVM and Image Segmentation
,
2004
.
[5]
Kevin Amaratunga,et al.
AUTOMATIC ROAD DETECTION IN GRAYSCALE AERIAL IMAGES
,
2000
.
[6]
Hui Long,et al.
Urban road extraction from high-resolution optical satellite images
,
2005
.
[7]
Klaus Steinnocher,et al.
Influence of image fusion approaches on classification accuracy: a case study
,
2006
.