Road Extraction in High-resolution Remote Sensing Images Based on an Improved Variational Level Set Method
An improved variational level set method is proposed and applied to road extraction of high-resolution remote sensing images. The new model is a variational level set method which is adapted to extract objects of interest from complex background and is achieved by introducing three terms into GACV (Geodesic Aided C-V) model. The three terms are the target identification function constructed based on the color region,growing algorithm, the color gradient flow computed according to the Beltrami framework, and the penalizing term which serves as a metric to characterize how close the level set function is to a signed distance function. Experimental results show that the model can effectively extract roads from high-resolution remote sensing images, considerably reduce the interference of non-road targets, and has a certain practicality.
color region growing GACV model variational level set high-resolution remote sensing images road extraction
WANG Xili GU Dandan WANG Xiyuan
College of Computer Science, Shaanxi Normal University Xian, China School of Physics and Electrical Information, Ningxia University Yinchuan, China
国际会议
厦门
英文
98-102
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）